Name: home-based monitor for gait and activity analysis
Uploaded: 2019-08-08T13:30:00.000+00:00
Duration: 7 min 24 s
Description: Watch this Scientific Journal Video about Home-Based Monitor for Gait and Activity Analysis at JoVE.com

The authors thank Anne-Ga&#235;lle Le Moing, Am&#233;lie Moreaux, and Eric Dorveaux for their contribution to the development of this wearable magneto-inertial sensor and Jackie Wyatt for the review.

Any use of the device must be carried out in accordance with the rules established by the reference protocol, validated by the ethics committee and the national regulatory agencies of the country. The use of the device and the various elements attached to it must be done within the intended use described in the patient&#39;s manual.
NOTE: To be eligible to use of the WMIS, patient must be over 5 years old, be able to understand and follow the usage rules, provide informed consent, be affiliate or beneficiary of a social security scheme, and be able to comply with all protocol requirements. There are no specific exclusion criteria.
1. Preparing for the participant&#8217;s visit at the clinical center
<ol>
	<li>Check the suitcase contents: (1) the docking station to plug the sensors during the night for data uploading onto a USB key and recharging of batteries, (2) the power cord divided into two pieces to connect the docking station to a power supply, (3) the Ethernet cable to permit interface with a router, (4) the two sensors to permit daily activity recording, (5) bands for attachment of the sensors depending on the chosen configuration and ambulatory status of the participant (ankle-ankle: two ankle armbands with two stickers to distinguish the wearing side; ankle-wrist: one ankle armband and one bracelet; wrist-wheelchair: one bracelet and one wheelchair pocket), (6) one participant manual and one investigator manual, (7) one task reminder, (8) a screwdriver to enable replacement of the USB key, and (9) two blank USB keys. 
	NOTE: If an internet connection is available data transfer occurs automatically</li>
	<li>Print and prepare the assignment form to record the assignment of a device to a participant. This will enable data reconciliation with the subject ID.</li>
</ol>
2. Training of the subject during the first visit
<ol>
	<li>Positioning of this WMIS
	<ol>
		<li>Based on the data that the researchers are seeking as well as the patient&#8217;s ambulatory status, use different configurations for the placement of the sensors.
		<ol>
			<li>For ambulant patients, fix the two sensors either on a wrist and an ankle for upper and lower limb activity recording or on both ankles for only lower limbs activity.</li>
			<li>For non-ambulant patients, fix one sensor on the wrist and the other one on the wheelchair.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Explain positioning of the sensors to the participant.
	<ol>
		<li>For ambulant participants, fix the two sensors either on a wrist and an ankle or on both ankles.
		<ol>
			<li>For the wrist-ankle configuration, place one sensor on the wrist of the dominant hand using the provided bracelet so that the waves are pointing toward the fingers. Place the second sensor on the ankle, on the same side as the wrist sensor, above the external malleolus with the waves pointing in the forward direction. 
			NOTE: The sensor must be placed on top of the wrist.</li>
			<li>For the ankle-ankle configuration, place a sensor on each ankle, above the external malleolus with the waves pointing in the forward direction. 
			NOTE: Stickers should be placed on the sensors to indicate the wearing side.</li>
		</ol>
		</li>
		<li>For non-ambulant participants, fix one sensor on the wrist and the other one on the wheelchair.
		<ol>
			<li>For the wrist-wheelchair configuration, place one sensor on the wrist of the participant&#8217;s dominant hand using the provided bracelet so that the waves are pointing toward the fingers. Place the second sensor in the bag provided. Attach it on a safe place to the wheelchair. 
			NOTE: Independently of the used configuration, do not switch sensors around. The sensors should fit snuggly, but not too tightly to the wrist and/or ankle to prevent them from spinning around.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Explain daily routine for sensors&#8217; use to the participant.
	<ol>
		<li>Evening routine
		<ol>
			<li>Plug the docking station into the power supply. Attach the docking station to the router if an internet connection is available. Insert the sensors into the docking station.</li>
			<li>Localize the two light-emitting diodes (LEDs) on the docking station which indicate the status of the sensors. Once plugged into a power source, be sure the station beeps and the diodes become orange to indicate that the sensor batteries are charging, and that data is being downloaded from the sensors to the USB drive. 
			NOTE: If the LEDS are still blinking after 5 min, restart the procedure from beginning. If the issue persists, contact the clinical site team.</li>
		</ol>
		</li>
		<li>Morning routine
		<ol>
			<li>Verify that the LEDs are green, indicating that the sensors batteries are fully charged, and that data has been cleared from the sensors&#8217; memory. Remove the sensors from the docking station. Wear the sensors in the configuration demonstrated by the evaluator. 
			NOTE: If LED on one or both sensors is orange after two consecutive days, contact the clinical center.</li>
		</ol>
		</li>
		<li>Daytime routine
		<ol>
			<li>Wear the sensors the entire day and place the sensors back on the docking station at the end of the day. 
			NOTE: Remove the sensors during activities involving water, special medical examinations (e.g., magnetic resonance imaging [MRI], CT-scan, X-ray) or any activity that could damage them, and keep them in a safe place on a firm surface. Resume wearing the sensors after the activity.</li>
		</ol>
		</li>
		<li>At the end of the recording period, tidy up all the device items in the suitcase and bring back the device to the clinical center. 
		NOTE: Encourage the participant to engage in normal daily activities wearing as much as possible the sensors.</li>
	</ol>
	</li>
	<li>Complete a dedicated assignment form.</li>
</ol>
3. Data collection and analysis
<ol>
	<li>Data collection 
	NOTE: The sensors record signals continuously for up to 16 h and store the information in an internal memory (Movie 1). The docking station enables the downloading of data stored in the sensors at the end of each day and charging of the batteries during the night. Data downloaded to the docking station is stored on a USB drive that can only be accessed by the evaluators.
	<ol>
		<li>A standard 64 GB USB drive can hold up to 3 months of daily recording information (approximately 16 h/day). Provide higher or lower capacity USB drives to adjust as closely as possible to the constraints of the protocols.</li>
		<li>If the docking station is not connected to internet, have the evaluator remove the USB drive from the docking station (with the specific screwdriver contained in the suitcase) and replace it with a blank one at the end of the recording period. The USB drive should be sent to the support team for analysis. 
		NOTE: If the docking station is connected to the internet, data are uploaded to cloud storage. Thus, there is no need to change the USB drive, as all data are automatically deleted from the USB drive once the files are uploaded into the cloud.</li>
	</ol>
	</li>
	<li>Data analysis
	<ol>
		<li>At selected time points during the study, extract data from the cloud storage and analyze data using a dedicated algorithm. Adjust the analysis periods and the monitoring reports based on the clinical study.</li>
	</ol>
	</li>
</ol>

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Charlotte Lilien, Teresa Gidaro, Andreea Seferian, and Erwan Gasnier are employees at the Institute of Myology and have no affiliation with Sysnav. Laurent Servais is an employee at the Institute of Myology and at the CHRMN Li&#232;ge and has no affiliation with Sysnav. Marc Grelet is employee of Sysnav. David Vissi&#232;re is a founder of Sysnav.

In the past decade, a number of different systems have been developed, such as an activity monitor (Table of Materials [IV]), which uses accelerometric sensors to monitor activities of daily life for energy expenditure quantification13. A triaxial accelerometer (Table of Materials [V]) was used by Tanaka et al.14 to monitor activity of preschool children. Lau et al.15 showed through the combination of a dual-accelerometer (Table of Materials [VI]) and a gyroscope (Table of Materials [VII]) that gait spatiotemporal characteristics can be precisely determined by the use of inertial sensors. Zijlstra et al.16 analyzed trunk and 289 compensatory movements of the pelvis occurred during gait using a body-fixed sensor. Most devices used for gait analyses are three-axis accelerometers17,18,19 (Table of Materials [VIII, IX, X]). In addition to accelerometers, our WMIS includes three gyroscopes, a magnetometer, and a barometer. Inertial sensors constitute an interesting technology for evaluation of the motricity of neuromuscular patients due to the difficulty of assessing their reduced movements and their abnormal gaits. Our WMIS can be used to evaluate even the most severely impaired patients, and unlike scales and other tools, provides objective and reliable data. Indeed, the precise measure of strides cannot be achieved with simple wrist worn inertial sensors but requires highly stable and precisely calibrated inertial sensors sampled at a high frequency and placed on the lower limb in order to compute stride trajectory. The same is true of other exploratory outcomes, in ambulant patients, like stair climbing or falls.
We demonstrated that precise estimate of foot trajectory in ambulant DMD is feasible by using our WMIS, which can be worn during daily life. The validated 6 MWT3 and the North Star Ambulatory Assessment (NSAA)20 have been correlated with our device&#8217;s variables describing spontaneous walk during two weeks recording and are sensitive to change in the DMD population over a 6 month period.
Upper limb activity discrimination during the use of the wheelchair can be challenging. Accelerometry permits the monitoring of physical activity either directly related to wheelchair21 or upper limb movements during wheelchair movements22 (Table of Materials [XI]). By placing a sensor on the wheelchair and using data from the three-axial accelerometer, gyroscope, and barometer, our WMIS can distinguish upper limb activity from wheelchair and caregiver movements and precisely quantify even very weak movements.
The small size of our sensors is in line with recommendations by Ciuti et al.23 who noted that the miniaturization of sensors allows new applications in various fields. Improvements in technology now permit easy use, access and promote home-based monitoring of patients through data transmission systems integrated into smart devices, such as smartphones24,25,26. However, sensors in smartphones are not calibrated to be used as a clinical outcome measure, and the variability over time is not controlled. Thus, smartphones or tablets can be used for patient-reported outcome measures (Table of Materials [XII]) but not as a device to measure movements precisely and continuously.
Several limitations are associated with our device and its protocol. One of the main challenges with continuous home recording is that patient compliance tends to decrease over time. In order to tackle this problem, we had to find the minimal period of time with the lowest variability of the defined variables that can be considered clinically significant for a patient. This was defined as 180 hours of recording, which corresponds to two weeks27.
A critical step of the protocol is the training. The patient must be trained to use the device, and it is crucial that the evaluator providing the device and the training has first been trained adequately. The best way to optimize the training of the clinical team and the patients to ensure consistent use of the device is by face-to-face training.
Our WMIS will find applications outside the neuromuscular field, for example in evaluation of patients with multiple sclerosis or Parkinson disease, in which other less sensitive and reliable devices have already been tested28,29. Qualification processes taking into account measure reliability, variability, confounding factors, minimally meaningful differences, and sensitivity to change must be performed for these subjects as they have been carried out in ambulant patients with DMD described here.

A number of potential therapies are in development for treatment of genetic neuromuscular diseases. These diseases include Duchenne muscular dystrophy (DMD) and spinal muscular atrophy (SMA) type 3. Subjects with these diseases present initially with proximal lower limb weakness that leads to progressive difficulties in ambulation. The final step in translational research is the demonstration of efficacy of a potential treatment or approach in a clinical trial. Specific, quantifiable, objective, and reliable measures are required. The importance of such measures was recently emphasized by the failure of the phase IIb ataluren trial1 and the phase III Biomarin trial2. One of the likely explanations for these failures was the variability and the nonlinear evolution of the primary outcome measure of these trials, the 6-minute walk test3 (6 MWT). Increasing reliability and sensitivity to the change of outcome measures and the understanding of the factors leading to their variation could contribute to decrease the number of trial failures related to the main outcome measures.
One of the limitations of the current outcomes is the subjectivity of the assessment. To further increase the objectivity of assessments, Heberer et al.4 showed that through a marker set and the use of a gait analysis software, there was a significant increase in stride length in patients treated with steroids compared with the na&#239;ve group. Hip joint kinetics are early markers of proximal weakness in patients with DMD and are responsive to change with steroid intervention, which is the only available treatment for these patients. Gait laboratories are, however, only available in large clinics. Furthermore, laboratory evaluations are point assessments, and a patient&#8217;s condition may greatly vary on a day to day basis due to factors such as fatigue, motivation, and intercurrent illness.
The use of continuous and home-based measurement should achieve both a more objective and a more globally representative assessment. In other fields of neurology, for instance Parkinson5 or multiple sclerosis6, several studies have assessed the feasibility, reliability, and consistency with other measures of different sensors including accelerometers with or without gyrometers or magnetometers, yet none of these devices is currently a gold standard for evaluation of patients during clinical trials. In the field of neuromuscular diseases, there is currently no validated method for continuous home monitoring of patients. In recent years, through a close collaboration between clinicians and engineers, the Institute of Myology in Paris has developed several devices for upper limb assessment to precisely evaluate upper limb strength and function7,8,9. A wearable magneto-inertial sensor (WMIS; i.e., ActiMyo) has been developed in collaboration with a company specialized in navigation systems. Initially a monitoring device dedicated to non-ambulant subjects with neuromuscular disorders such as DMD and SMA10,11, the same device has now been used to monitor ambulant patients in two different configurations: sensors on both ankles or one sensor at the wrist and the other one at the ankle. The configuration for a non-ambulant population is composed of a sensor at the wheelchair and the other one at the wrist.
This WMIS is able to precisely capture and quantify all movements of the limb on which it is placed. The measuring principle is based on the use of microelectromechanical system (MEMS) inertial sensors and magnetometers operated through magneto-inertial equations. Dedicated algorithms allow precise qualification and quantification of patients&#8217; movements in a non-controlled environment.&#160;
The overall goal of the method is to provide identification and quantification of any movement produced by a patient over a pre-defined period of time, and to integrate these measures into disease-specific outcome measures representative of the patient&#8217;s condition over a period of time.
To effectively assess ambulant and non-ambulant patients with movement disorders at home, the device must be provided to the patient by a trained evaluator who is responsible for making sure that the instructions have been understood. An investigator and a patient manual are provided with the device. This WMIS is currently being used as an exploratory outcome measure in a number of clinical trials for neuromuscular and neurologic diseases (NCT03351270, NCT02780492, NCT01385917, NCT03039686, NCT03368742, NCT02500381). Specific procedures adapted to the pathology and/or to the clinical trial design have been developed.

<table><tbody><tr><td>ActiMyo Sensors</td><td>Sysnav</td><td>SF-000080</td><td>Wearable magneto-inertal sensors attached to the patient for movment recording</td></tr><tr><td>Helen Hayes marker set</td><td>Vicon</td><td>NA</td><td>Whole body jumpsuit with predefined Vicon&#039;s spots</td></tr><tr><td>OrthoTrak (Motion Analysis, Santa Rosa, CA, USA)</td><td>Motion Lab Systems</td><td></td><td>Gait analysis software</td></tr><tr><td>ActiGraph</td><td>ActiGraph Corp</td><td>GTM1</td><td>Activity monitor, used by researchers to capture and record continuous, high resolution physical activity and sleep/wake information</td></tr><tr><td>ActivTracer GMS LTD</td><td>GMS Co. Ltd Japan</td><td>AC-301A</td><td>Triaxial accelerometer</td></tr><tr><td>ADXL202E dual-accelerometer</td><td>Analog Devices</td><td>ADXL212AEZ</td><td>High precision, low power, complete dual axis accelerometer with signal conditioned, duty cycle modulated outputs, all on a single monolithic IC.</td></tr><tr><td>ENC-03J gyroscope</td><td>Murata Electronics</td><td>ENC-03J</td><td>Vibration Sensors</td></tr><tr><td>DynaPort MiniMod</td><td>MCROBERTS</td><td></td><td>Small and light case containing a tri-axial accelerometer, a rechargeable battery, an USB connection, and raw data storage on a MicroSD card</td></tr><tr><td>MM-2860 Sunhayato</td><td>Sunhayato</td><td>MM-2860</td><td>3-axis accelerometer</td></tr><tr><td>MicroStone MA3-10Ac</td><td>MA3-04AC</td><td>Microstone Co.</td><td>Acceleration sensors</td></tr><tr><td>RT3 Activity monitor</td><td>Abledata</td><td>NA</td><td>Triaxial accelerometer</td></tr><tr><td>Aparito</td><td>aparito</td><td>NA</td><td>Wearables and disease specific mobile apps to deliver patient monitoring outside of the hospital; Elin Davies, Aparito: https://www.aparito.com/</td></tr><tr><td>Docking station</td><td>Sysnav</td><td>SF-000118</td><td></td></tr><tr><td>Sensor</td><td>Sysnav</td><td>SF-000080</td><td></td></tr><tr><td>Bracelet&lt;br/&gt; (black/grey L)&lt;br/&gt; (black/grey S) (black/yellow L) (black/yellow S)</td><td>Sysnav</td><td>ZZ-000093 ZZ-000094 ZZ-000247 ZZ-000248</td><td></td></tr><tr><td>Patient manual</td><td>Sysnav</td><td>FD-000086</td><td></td></tr><tr><td>Ethernet cable (2 m max.)</td><td>Sysnav</td><td>IC-000458</td><td></td></tr><tr><td>Power cable&lt;br/&gt; (EU)&lt;br/&gt; (UK)&lt;br/&gt; (US)</td><td>Sysnav</td><td>ZE-000440 ZE-000441 ZE-000442</td><td></td></tr><tr><td>Power supply unit</td><td>Sysnav</td><td>ZE-000443</td><td></td></tr><tr><td>Ankle strap</td><td>Sysnav</td><td>ZZ-000462</td><td></td></tr><tr><td>Small bag</td><td>Sysnav</td><td>ZZ-000033</td><td></td></tr></tbody></table>

home-based monitor for gait and activity analysis

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical evaluators. These measures are slightly subjective and are performed during a visit to a clinic or hospital and constitute therefore a point assessment. Point assessments can be influenced by daily patient condition or factors such as fatigue, motivation, and intercurrent illness. To enable home-based monitoring of gait and activity, a wearable magneto-inertial sensor (WMIS) has been developed. This device is a movement monitor composed of two very light watch-like sensors and a docking station. Each sensor contains a tri-axial accelerometer, gyroscope, magnetometer, and a barometer that record linear acceleration, angular velocity, the magnetic field of the movement in all directions, and barometric altitude, respectively. The sensors can be worn on the wrist, ankle, or wheelchair to record the subject&#8217;s movements during the day. The docking station enables data uploading and recharging of sensor batteries during the night. Data are analyzed using proprietary algorithms to compute parameters representative of the type and intensity of the performed movement. This WMIS can record a set of digital biomarkers, including cumulative variables, such as total number of meters walked, and descriptive gait variables, such as the percentage of the most rapid or longest stride that represents the top performance of patient over a predefined period of time.

Data presented here were acquired during clinical trials approved by the ethics committee and the French Regulatory Agency. All patient representatives signed an informed consent.
This WMIS was first used in a clinical study setting in 2012 for controlled and home-based monitoring of upper-limb movements in non-ambulant DMD patients (NCT01611597), which demonstrated the autonomy and feasibility of device use10. Variables, such as norm of angular velocity, the ratio of the vertical component of the acceleration to the overall acceleration, the elevation rate, and the computed power, were identified to clinically characterize the upper limb activity of patients in a controlled environment (Table 1). In a second step, these variables were correlated with the efficacy of patients during a standardized and validated task, which also allowed testing of device reliability10. A more complete validation of variables relevant to non-ambulatory subjects is ongoing.
The validation process in ambulant patients is much more advanced. Recently, the committee for medicinal products for human use (CHMP) wrapped up the public consultation for the qualification of the 95th centile stride velocity (95CSV) as a validated secondary outcome in ambulant DMD patients and the final adoption of qualification opinion is pending. This WMIS allows the identification and measure (height, length, velocity) of each single stride (Figure 1) over a long period of time and enables analysis of the distribution of all captured strides, which allows calculation of centile of stride speed and length. The 95CSV appeared to be the most sensitive variable to changes in ambulant DMD patients. The 95CSV provides a sensitive outcome measure that allows a small number of patients per group in a controlled trial. Several other parameters measureable by this WMIS are less sensitive to change than 95CSV but are more closely related to quality of life such as distance walked per hour and the number of falls per hour (Table 1).
The precision of the gait trajectory was tested initially in controls, using an optokinetic system that confirmed excellent agreement between trajectories measured by optokinetic system and magnetoinertial sensor12. In patients, we assessed the agreement between the distances measured by this WMIS during a 6 MWT to the distance measured by physiotherapists. Figure 2 illustrates the ankle trajectory and orientation reconstructed from our WMIS measurements during one lap of a 6 MWT. For this study, data were obtained from 23 ambulant DMD patients (NCT02780492) during 31 6 MWT (some patients performed a second test 6 months later). Results are displayed in Figure 3. The difference between the distance measured by our WMIS and the reference 6 MWT (after correction for the length of the turn around the cones, see Figure 2) was within 5%.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59668/59668fig1.jpg" /> 
Figure 1: Representation of the stride length by the black line which is the computed trajectory of the ankle position during walk reconstructed by this WMIS. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/59668/59668fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59668/59668fig2.jpg" /> 
Figure 2: Representation of the trajectory during one lap of a 6 MWT reconstructed by this WMIS. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/59668/59668fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59668/59668fig3.jpg" /> 
Figure 3: The 6 MWT distance calculated using this WMIS (y axis) versus the distance measured by the physiotherapist during the same test (x axis) for 31 6 MWT performed by 23 patients. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/59668/59668fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Movie 1" src="/files/ftp_upload/59668/59668mov1.jpg" /> 
Movie 1: Reconstruction of the ankle trajectory during walking by using this WMIS. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/59668/Video1.mp4" target="_blank">Please click here to view this video. (Right-click to download.)</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Walking parameters</td>
			<td>Upper limb parameters</td>
		</tr>
		<tr>
			<td>Percentage of time spent walking (%)</td>
			<td>Percentage of time involving upper limb activity (%)</td>
		</tr>
		<tr>
			<td>Walked distance per hour (m/h)</td>
			<td>Median angular velocity of the wrist (&#176;/s)</td>
		</tr>
		<tr>
			<td>Median stride speed (m/s)</td>
			<td>95th angular velocity of the wrist (&#176;/s)</td>
		</tr>
		<tr>
			<td>95CSV (m/s)</td>
			<td>Median vertical acceleration (g)</td>
		</tr>
		<tr>
			<td>Median stride length (m)</td>
			<td>95th vertical acceleration (g)</td>
		</tr>
		<tr>
			<td>95th percentile of the stride length (m)</td>
			<td>Median power (W/kg)</td>
		</tr>
		<tr>
			<td>Number of falls per hour (count/h)</td>
			<td>95th power (W/kg)</td>
		</tr>
	</tbody>
</table>
Table 1: List of parameters.

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Home-Based Monitor for Gait and Activity Analysis

This innovative device uses magneto-inertial sensors to permit gait and activity analysis in uncontrolled environments. Currently in the qualification process as an outcome measure in the European Medical Agency, one of the applications will be to serve as a clinical endpoint in clinical trials in neuromuscular diseases.

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical ...

home-based-monitor-for-gait-and-activity-analysis

Universidad San Sebastian

Research

JoVE Journal

Bioengineering

6.7K Views.  Institut de Myologie. This innovative device uses magneto-inertial sensors to permit gait and activity analysis in uncontrolled environments. Currently in the qualification process as an outcome measure in the European Medical Agency, one of the applications will be to serve as a clinical endpoint in clinical trials in neuromuscular diseases.

Video: Home-Based Monitor for Gait and Activity Analysis

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Behavior

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Nordic walking (NW) has become a safe and simple form of exercise in recent years, and in studying this gait pattern, various data collection techniques have been employed, each with positives and negatives. The aim was to determine the effect of NW on older adult gait and posture and to determine optimal use of different data collection systems in both short and long duration analysis. Gait and posture during NW and normal walking were assessed in 17 healthy older adults (age: 69 &#177; 7.3). Participants performed two trials of 6 Minute Walk Tests (6MWT) (1 with poles (WP) and 1 without poles (NP)) and 6 trials of a 5m walk (3 WP and 3 NP). Motion was recorded using two systems, a 6-sensor accelerometry system and an 8-camera 3-dimensional motion capture system, in order to quantify spatial-temporal, kinematic, and kinetic parameters.
With both systems, participants demonstrated increased stride length and double support and decreased gait speed and cadence WP compared to NP (p &#60;0.05). Also, with motion capture, larger single support time was found WP (p &#60;0.05). With 3-D capture, smaller hip power generation and moments of force were found at heel contact and pre-swing as well as smaller knee power absorption at heel contact, pre-swing, and terminal swing WP compared to NP, when assessed over one cycle (p &#60;0.05). Also, WP yielded smaller moments of force at heel contact and terminal swing along with larger moments at mid-stance of a gait cycle (p &#60;0.05). No changes were found for posture.
NW seems appropriate for promoting a normal gait pattern in older adults. Three-dimensional motion capture should primarily be used during short duration gait analysis (i.e. single gait cycle), while accelerometry systems should be primarily employed in instances requiring longer duration analysis such as during the 6MWT.

The aim was to substantiate optimal use of data collection techniques for Nordic walking gait and posture analysis. Three-dimensional motion capture should be used during short duration analysis (i.e. single gait cycle), while accelerometry should be employed for longer duration analysis (i.e. repeated cycles) like a 6 Minute Walk Test.

Nordic walking (NW) has become a safe and simple form of exercise in recent years, and in studying this gait pattern, various data collection techniques ...

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Three-dimensional gait analysis (3DGA) is shown to be a useful clinical tool for the evaluation of gait abnormality due to movement disorders. However, the use of 3DGA in actual clinics remains uncommon. Possible reasons could include the time-consuming measurement process and difficulties in understanding measurement results, which are often presented using a large number of graphs. Here we present a clinician-friendly 3DGA method developed to facilitate the clinical use of 3DGA. This method consists of simplified preparation and measurement processes that can be performed in a short time period in clinical settings and intuitive results presentation to facilitate clinicians&#39; understanding of results. The quick, simplified measurement procedure is achieved by the use of minimum markers and measurement of patients on a treadmill. To facilitate clinician understanding, results are presented in figures based on the clinicians&#39; perspective. A Lissajous overview picture (LOP), which shows the trajectories of all markers from a holistic viewpoint, is used to facilitate intuitive understanding of gait patterns. Abnormal gait pattern indices, which are based on clinicians&#39; perspectives in gait evaluation and standardized using the data of healthy subjects, are used to evaluate the extent of typical abnormal gait patterns in stroke patients. A graph depicting the analysis of the toe clearance strategy, which depicts how patients rely on normal and compensatory strategies to achieve toe clearance, is also presented. These methods could facilitate implementation of 3DGA in clinical settings and further encourage development of measurement strategies from the clinician&#39;s point of view.

In this study, a clinician-friendly three-dimensional gait analysis method, which was designed to be performed in the rehabilitation clinic, is presented. The method consists of a simplified measurement method and intuitive figures to facilitate clinicians&#39; understanding of the results.

Three-dimensional gait analysis (3DGA) is shown to be a useful clinical tool for the evaluation of gait abnormality due to movement disorders. However, ...

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to join existing programs at cardiac rehabilitation centers. The unique remote patient monitoring system presented here enables healthcare providers to monitor CR patients at home in real-time and at low cost. The system combines mobile technology, artificial intelligence, and supportive services, expanding the delivery of medical care to the patient&#39;s home. The primary aim of the study is to increase the long-term adherence to physical activity in patients who participate in CR via the addition of a home-based digitally monitored CR component to the standard CR program in patients with ischemic heart disease (IHD), with the idea of forming new habitual health behaviors and increasing the long-term motivation for physical exercise (PE) habits at home. Secondary aims are to assess the program&#39;s impact on the physical activity level measured by average steps per day, minutes of exercise per week, blood pressure, metabolic parameters, body mass index, and waist-to-hip ratio, as well as a quality-of-life (QoL) questionnaire.The study has two arms: (1) home-based monitored exercise using a smart digital garment and wristband, in addition to motivation and reinforcement via text messages; (2) standard CR facility-based exercise. The study design is a randomized, controlled trial comparing standard CR to a home-based monitoring and reinforcement program. The study program is designed for 12 weeks.Clinical tests and anthropometric measurements are performed before and after the study, measuring height, weight, waist circumference, visceral fat and body mass index (BMI), blood pressure, and HbA1c and lipid profile. Patients have to complete a baseline survey including socio-demographic characteristics and QoL questionnaire SF-36. At the end of the study, patients complete a survey regarding the use of the smart digital garment&#39;s benefits and usability. The study is currently underway.

The aim is to promote a new approach to cardiac rehabilitation (CR), using a unique remote patient monitoring system that will enable healthcare providers to monitor CR patients at home at low cost, with the intention of making CR services more accessible and improving compliance. The study is currently underway.

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to ...

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Gait analysis is used to quantify changes in motor function in many rodent models of disease. Despite the importance of assessing gait and motor function in many areas of research, the available commercial options have several limitations such as high cost and lack of accessible, open code. To address these issues, we developed PrAnCER, Paw-Print Analysis of Contrast-Enhanced Recordings, for automated quantification of gait. The contrast-enhanced recordings are produced by using a translucent floor that obscures objects not in contact with the surface, effectively isolating the rat&#8217;s paw prints as it walks. Using these videos, our simple software program reliably measures a variety of spatiotemporal gait parameters. To demonstrate that PrAnCER can accurately detect changes in motor function, we employed a haloperidol model of Parkinson&#8217;s disease (PD). We tested rats at two doses of haloperidol: high dose (0.30 mg/kg) and low dose (0.15 mg/kg). Haloperidol significantly increased stance duration and hind paw contact area in the low dose condition, as might be expected in a PD model. In the high dose condition, we found a similar increase in contact area but also an unexpected increase in stride length. With further research, we found that this increased stride length is consistent with the bracing-escape phenomenon commonly observed at higher doses of haloperidol. Thus, PrAnCER was able to detect both expected and unexpected changes in rodent gait patterns. Additionally, we confirmed that PrAnCER is consistent and accurate when compared with manual scoring of gait parameters.

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

We describe a novel gait analysis system, Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER), an open-access automated system for the quantification of gait characteristics in rats that utilizes a novel semitransparent floor to automatically quantify gait. This system was validated using the haloperidol model of Parkinson&#8217;s Disease.

Gait analysis is used to quantify changes in motor function in many rodent models of disease. Despite the importance of assessing gait and motor function ...

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Spatial and temporal characteristics of human walking are frequently evaluated to identify possible gait impairments, mainly in orthopedic and neurological patients1-4, but also in healthy older adults5,6. The quantitative gait analysis described in this protocol is performed with a recently-introduced photoelectric system (see&#160;Materials table) which has the potential to be used in the clinic because it is portable, easy to set up (no subject preparation is required before a test), and does not require maintenance and sensor calibration. The photoelectric system consists of series of high-density floor-based photoelectric cells with light-emitting and light-receiving diodes that are placed parallel to each other to create a corridor, and are oriented perpendicular to the line of progression7. The system simply detects interruptions in light signal, for instance due to the presence of feet within the recording area. Temporal gait parameters and 1D spatial coordinates of consecutive steps are subsequently calculated to provide common gait parameters such as step length, single limb support and walking velocity8, whose validity against a criterion instrument has recently been demonstrated7,9. The measurement procedures are very straightforward; a single patient can be tested in less than 5 min&#160;and a comprehensive report can be generated in less than 1&#160;min.

This protocol is used to evaluate spatial and temporal gait variables of neurological/orthopedic patients and older persons by means of a recently-introduced floor-based photocell system.

Spatial and temporal characteristics of human walking are frequently evaluated to identify possible gait impairments, mainly in orthopedic and ...

Medicine

Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty

Approximately 1.2 million people in the United States live with HIV infection. Medical advancements have increased the life expectancy and this cohort is aging. HIV-positive individuals have a high incidence of frailty (~20%) characterized by depression and sedentary behavior. Exercise would be healthy, but due to the frail status of many HIV-positive individuals, conventional exercise is too taxing. The aim of this study was to evaluate the effectiveness and acceptability of a novel game-based training program (exergame) in ameliorating some aspects of frailty in HIV-infected individuals. Ten older people living with HIV were enrolled in an exergame intervention. Patients performed balance exercises such as weight shifting, ankle reaching, and obstacle crossing. Real-time visual/audio lower-extremity joint motion feedback was provided using wearable sensors to assist feedback and encourage subjects to accurately execute each exercise task. Patients trained twice a week for 45 min for 6 weeks. Changes in balance, gait, psychosocial parameters and quality of life parameters were assessed at the beginning, midterm and at conclusion of the training program. Ten patients completed the study and their results analyzed. The mean age was 57.2 &#177; 9.2 years. The participants showed a significant reduction in center of mass sway (78.2%, p = .045) during the semi-tandem balance stance with eyes closed and showed a significant increase in gait speed during a dual task motor-cognitive assessment (9.3%, p = .048) with an increase in stride velocity of over 0.1 m/sec. A significant reduction in reported pain occurred (43.5%, p = .041). Preliminary results of this exergame intervention show promise in improving balance and mobility while requiring older people living with HIV to be more active. The exergame can be continued at home and may have long term as well as short-term benefits for ameliorating frailty associated with HIV infection.

Persons infected with HIV are often frail, depressed and live a sedentary lifestyle for which conventional exercise is too taxing. Here, we present an exercise protocol that ameliorates aspects of frailty in HIV-infected persons. An exergame integrating cognitive control was developed using biosensors that measured balance, weight-shifting and obstacle crossing.

Approximately 1.2 million people in the United States live with HIV infection. Medical advancements have increased the life expectancy and this cohort is ...

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to assess human gait. The primary aim of this manuscript is to test the concurrent validity of kinematic measures recorded by a smartphone application in comparison to a 3D motion capture system in the sagittal plane. The secondary aim was to develop a protocol for clinicians on the set up of the smartphone camera for video movement analysis.
The sagittal plane knee angle was measured during heel strike and toe off events using the smart phone app and a 3D motion-capture system in 32 healthy subjects. Three trials were performed at near (2-m) and far (4-m) smartphone camera distances. The order of the distances was randomized. Regression analysis was performed to estimate the height of the camera based on either the subject&#39;s height or leg length.
Absolute measurement errors were least during toe off (3.12 &#177; 5.44 degrees) compared to heel strike (5.81 &#177; 5.26 degrees). There were significant (p &#60; 0.05) but moderate agreements between the application and 3D motion capture measures of knee angles. There were also no significant (p &#62; 0.05) differences between the absolute measurement errors between the two camera positions. The measurement errors averaged between 3 - 5 degrees during toe off and heel strike events of the gait cycle.
The use of smartphone apps can be a useful tool in the clinic for performing gait or human movement analysis. Further studies are needed to establish the accuracy in measuring movements of the upper extremity and trunk.

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

This manuscript describes the method to test the concurrent validity of kinematic measures recorded by the smartphone application in comparison to a 3D motion capture system in the sagittal plane. This protocol will enable clinicians to set up smartphones for video capture of human movement.

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to ...

Evaluating Postural Control and Lower-extremity Muscle Activation in Individuals with Chronic Ankle Instability

Computerized dynamic posturography (CDP) is an objective technique for the evaluation of postural stability under static and dynamic conditions and perturbation. CDP is based on the inverted pendulum model that traces the interrelationship between the center of pressure and the center of gravity. CDP can be used to analyze the proportions of vision, proprioception, and vestibular sensation to maintain postural stability. The following characters define chronic ankle instability (CAI): persistent ankle pain, swelling, the feeling of &#8220;giving way,&#8221; and self-reported disability. Postural stability and fibular muscle activation level in individuals with CAI decreased due to lateral ankle ligament complex injuries. Few studies have used CDP to explore the postural stability of individuals with CAI. Studies that investigate postural stability and related muscle activation by using synchronized CDP with surface electromyography are lacking. This CDP protocol includes a sensory organization test (SOT), a motor control test (MCT), and an adaption test (ADT), as well as tests that measure unilateral stance (US) and limit of stability (LOS). The surface electromyography system is synchronized with CDP to collect data on lower limb muscle activation during measurement. This protocol presents a novel approach for evaluating the coordination of the visual, somatosensory, and vestibular systems and related muscle activation to maintain postural stability. Moreover, it provides new insights into the neuromuscular control of individuals with CAI when coping with real complex environments.

Individuals with chronic ankle instability (CAI) exhibit postural control deficiency and delayed muscle activation of lower extremities. Computerized dynamic posturography combined with surface electromyography provides insights into the coordination of the visual, somatosensory, and vestibular systems with muscle activation regulation to maintain postural stability in individuals with CAI.

Computerized dynamic posturography (CDP) is an objective technique for the evaluation of postural stability under static and dynamic conditions and ...