This work was supported by a Career Development Award # [E7503W] and a Merit Award # [RX002413-01A2] from the United States (U.S.) Department of Veterans Affairs Rehabilitation Research and Development Service. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

All methods described here have been approved by the Institutional Review Board at the Corporal Michael J. Crescenz VA Medical Center in Philadelphia, PA.
1. Installation and Set-Up of a Real-Time Locating System (RTLS)
<ol>
	<li>Review facility policies, safety, and personal information protections for residents with facility stakeholders. Determine whether written or verbal support on the use of RTLS in the facility is required. In discussions with stakeholders include local protocols and procedures (e.g., local facility technology waivers, union sign-off, etc.) and a project timeline.12 
	NOTE: Update protocols, procedures, and timeline as they change over the course of the project, meeting with stakeholders and acquiring sign-off from interested parties.</li>
	<li>Obtain institutional review board approval including a HIPAA waiver to review medical charts prior to obtaining consent from eligible residents.</li>
	<li>Equip the desired area of study with a RTLS (see Figure 1). Mount sensors in the upper corners of all common rooms and hallways to triangulate resident location and movement in real-time.
	<ol>
		<li>Point sensors toward the middle of the area to utilize their antenna pattern which is +/- 90 degrees in the azimuth (Horizontal) and +/- 45 degrees in the elevation (Vertical). Tilt the face of the sensor downwards so that if a laser beam came out of the face of the sensor if would hit the opposite corner of the space about 5-6ft off the ground. Ensure the sensor is level by placing a level on the two plastic pegs on the top back of the sensor. 
		NOTE: For a typical communal area in a long-term care facility (about 10m x 13m or 1,000 square feet), four sensors are needed. These sensors will cover a larger area but this is dependent on the surrounding environment &#8211; e.g., walls and glass partitions in the area which may have an impact on transmissions.</li>
		<li>Each sensor needs a network cable running from the lower left port on the back of the sensor to the switch that the server is connected to; this cable is a Cat5e cable. With one sensor as the master, run the timing cable from the master to each other sensor, thus a star topology.
		<ol>
			<li>To do so, plug a shielded cat5e cable into any of the 6 available ports on the master and run it to each other sensor where it will be plugged into the upper right port of the 6 ports. Run cables above ceiling tiles. 
			NOTE: The number of sensors in the area determine the number of ports required for the power over Ethernet (POE) switch. Each sensor will require two ports. Multiple POE switches can be connected if needed.</li>
		</ol>
		</li>
		<li>Measure where the sensors are located in the area and choose an origin point on the sensor (e.g., the lower left corner so that moving north is the positive y axis and moving east is the positive x axis). Measure the x, y, and z of each sensor (with a laser distance measurer) in relation to this origin. Record the MAC address off the back of the sensor and keep to enter into the graphical user interface (GUI;a specialized software developed to manage the RTLS).</li>
	</ol>
	</li>
	<li>In the GUI, open Platform Control and click on Core Server to highlight it and then click start. Repeat this for the Service Controller. Click Apply and then OK.
	<ol>
		<li>Open the Service Installer and press next. Browse to C:\Ubisense Software and go into the Location Engine folder and highlight the &#8220;packages&#8221; folder. Click OK and next. Install all the services listed. Repeat this process again but go into the Platform folder and highlight the &#8220;packages&#8221; folder. Install all the services listed. Click on Service Manager and ensure all services appear as &#8220;running.&#8221;</li>
	</ol>
	</li>
	<li>Open the Site Manager and go to the Areas tab. Create a floor plan by opening notepad and specify the start and stop point of each wall by giving the x, y coordinates of the starting point followed by the ending point. Save the file as a .dat file. After the last set of points (0,0) press enter.
	<ol>
		<li>In the Areas tab go to Walls&#62;Load Walls and load the .dat file. Go to Regions&#62;Set Origin and choose the lower left corner. Click the draw wall mode button and add a dummy wall anywhere inside the square. This tells the system where to compute the regions (inside vs. outside the region).</li>
		<li>Click Regions&#62;Compute Regions; this highlights the square blue. Delete the wall &#8211; by selecting the Wall Mode button and pressing delete. Go to Area&#62;Save Area and save the area. Go to the Cells tab and load the area by choosing it from the drop down Area box.</li>
		<li>Click the Add Extent button in the lower left corner. To use the defaults click Save. Right click on Site in the left column and choose New Geometry Cell. Click the Add Extent button and again use the defaults. Right click on the Geometry Cell and choose New Location Cell. Click the Add Extent button and use the defaults.</li>
	</ol>
	</li>
	<li>Open the Location Engine Configuration and load the area by going to MAP&#62;Load Area. Add a Location Engine cell which will be used to set up a cell of sensors by going to Cell&#62;New. There are no available sensors in left column; go to File&#62;Import Sensors and locate the .xsc file that located at: C:\Ubisense Software.
	<ol>
		<li>Viewing all of the sensors, click on a sensor and drag it anywhere onto the map. It will also be under Location Cell 0001; right click on it and go to Properties. Enter the x, y, and z for that particular sensor and its MAC address. Do not enter in anything for yaw, pitch, or roll. Repeat this process for all other sensors and ensure the system places them correctly on the map.</li>
		<li>Click the Sensor Status tab; sensors are running &#8211; if not unplug and plug back in to the power source. Use the log tab to monitor the boot up process. Each sensor will download packets in groups of 100 and will eventually report sensor running. Refer back to the Sensor Status tab to make sure the sensors have booted and are running.</li>
		<li>Click on the incident power plot tab to examine the background noise level on each sensor. Let the graphs run. After a break, press the Set Thresholds button. This will set the Activity threshold on each sensor which will filter out background noise. Background noise is recommended below 1000.</li>
		<li>Right click on Location Engine Cell 00001&#62;Properties. On the Radio tab set the RF Power to 255, which is the max radio level. On the Geometry tab set the Ceiling to 5, the Floor to 0 and the Max Standard Error to 0.05. The ceiling is the max height of the space, the floor is min, and the max standard error is for filtering poor readings.</li>
	</ol>
	</li>
	<li>Pick up a compact or hang tag and go to the Tags tab in Location Engine Config and click Tag&#62;New. Enter tag number and set the upper Qos to 32, the same value as the lower Qos. These rates are the beaconing rates of the tag. Choose default information filter as the filter.
	<ol>
		<li>Click on the Sensors and Cells tab. Right click on Location Engine Cell 0001 and click Monitor. This sets the tags in the cell to transmit and never fall asleep. Press and hold the bottom middle of the compact tag and the middle of the hang tag for three seconds to turn on the tag. It is on when the light in the upper right hand corner is steady and begins to blink.</li>
		<li>Put the tag in the middle of the area where all sensors have it in the line of sight. Measure the x, y, and z of that spot in relation to the same origin on the sensor used before. Right click on any of three other sensors and choose Dual Calibration. Use the master as the reference, type in the tag number, type in the location measured and choose Next. After the calibration finishes save the values for all sensors.</li>
		<li>Run this above step again to ensure the values are +/- 2. Repeat this process for all the other sensors but do not save the master sensor values. If using a hang tag, rotate it so that the face of the tag is pointing between the master and another sensor being calibrated and make sure the tag is in a vertical position. The compact tag needs to be in one spot lying flat.</li>
		<li>Ensure the sensors are pointing correctly toward the center of the area and view the green angle of arrival lines converging on the tag. Click the TDOA box near the bottom of the window and view the time delay of arrival curves converging on the tag. Note that these lines and curves will not be perfect. Repeat the calibration if necessary. Then, following instructions from step 5.1, click the monitor flag off.</li>
		<li>Open the Map and load the Area under Area&#62;Load Area and view the tag on the map.</li>
	</ol>
	</li>
</ol>
2. Use the RTLS Tags to Locate and Track Residents in Real-Time
<ol>
	<li>Review medical charts to identify ambulatory (with/out an assistance device) residents or residents who can propel with their feet age 55 or over with CI/dementia. Obtain consent. Or, if the resident is unable to consent on their own, use contact information provided in the medical chart to contact their legally authorized representative (LAR) or next of kin (NOK).</li>
	<li>Outfit consenting residents with wrist or hang-tag (see Figure 1). To turn on the tag, place a magnet under the bottom right of the tag and wait for the light&#160; to blink continuously. Ensure the hang-tag it is not on backward or the signal will be attenuated. Attach the wrist tag to an area of the body with a small cross sectional area and more limited absorption of radio frequency energy and provides better tracking accuracy.</li>
	<li>Develop a protocol for health care staff to remove a resident tag during bathing and showering and train health care staff on these steps. Communicate a pre-determined location to health care staff where they may leave tags they find in the unit (e.g., behind the nurse&#8217;s station) in the event research staff are not there to retrieve them.</li>
	<li>Prior to putting the tag on the resident, in the Tag Association tab GUI (see Figure 2), assign each resident a random and unique &#34;patient ID&#34; number and input into the GUI. Using the number provided on the tag, input the Tag ID number associating it with the &#8220;patient ID.&#8221; The tag will be wirelessly tracked once assigned in the GUI. Keep position at &#34;origin&#34; but in &#34;allow tag swaps&#34;, select &#34;true,&#34; and then click save. 
	NOTE: If data are compromised the privacy and security of the residents is maintained as only a random identification number and x, y coordinates are available; these coordinates do not correspond to any home/institution, city/town, etc.</li>
	<li>Create a separate document saved on a secured server behind a firewall and a password-protected computer linking the residents&#39; personal information with their patient ID and tag ID.</li>
	<li>In Smart Space Config click view trace messages. Click the &#34;get trace messages. Examine the events for tag/resident location and movement. Click the log tab to ensure there are no error messages.</li>
	<li>Click the sensor status tab and view that all sensors are &#34;running&#34; (see Figure 4). If not, right click on the sensor and reboot. If timing or other statuses are noted after the reboot, check physical cables running to the problematic sensor.
	<ol>
		<li>Ensure all cables are plugged in to the POE switch and that timing and power cables are working on the specific sensor. For example, if the power cable is not working, there will be no light on the sensor and a new power cable is needed. If there is power, a new timing cable is needed.</li>
	</ol>
	</li>
	<li>In C: Ubisense Software system files, set up a folder on the server to access the raw daily CSV data files.</li>
	<li>Set up an automatic data backup system (external hard drive) and secure so it cannot be unplugged or moved from the server.</li>
	<li>In a data management program, smooth RTLS raw data using a 5-second moving average time window (based on time provided in x and y raw data coordinates) and a threshold of 0.7 m of movement (based on location provided in x and y raw data coordinates). 
	NOTE: This creates a stable series of coordinates, resembling the observed resident walking activity. To manage the jumps in data, when computing a day&#8217;s motion, only accrue distance and time (and path data) when time between points is less than 30 seconds.</li>
</ol>
3. Measuring Walking Activity and Wandering
<ol>
	<li>Download daily csv files into a data management/analysis program. 
	NOTE: Based on project aim, RTLS data can be reduced to hourly, daily, weekly, bi-weekly, and so forth. For the purposes of this project, data are averaged weekly (summed daily/7) to examine intra-individual changes in ambulation by week. Note that the number of daily samples available for each resident will vary based on their level of activity. Residents who are largely sedentary will have several hundred data points/day or less; residents who are more active will have more like several thousand data points/day.</li>
	<li>Calculate average walking distance, sustained walking distance, and sustained gait speed, and calculate the extent of changes in these measures over time using the raw location data provided (weekly averages of x, y coordinates).</li>
	<li>NOTE: Walking distance = average total number of meters walked per week [e.g., to calculate between each point: &#8730;(x2-x1)^2 + (y2-y1)^2], sustained walking distance = average number of continuous meters walked per week calculated only when the resident travels for at least 60 seconds with a stop not exceeding 30 seconds, gait speed = the average meters per second/week calculated during sustained walking only [to calculate between each point: &#8730;(x2-x1)^2 + (y2-y1)^2 and then t2-t1 to determine the time it takes to go this distance].</li>
	<li>Visually check all sensor light indicators on the RTLS sensors and tags once a day. Check all supplementary equipment provided (e.g., POE switches and timing boxes) for lights.
	<ol>
		<li>In the GUI, under &#34;map&#34; check to ensure all tagged residents are visible and being tracked each day (see Figure 5). If there is a resident missing on the map, click report to determine the last time the resident was seen by the system. Click hourly, daily, or weekly reports, which can also be filtered by Patient ID (see Figure 6). 
		NOTE: This can also be accomplished by reviewing daily CSV files for Patient ID numbers.</li>
		<li>When a tag is not working, replace the tag and/or check battery. When batteries are replaced, click on the associated tag and the &#8220;tag battery replaced&#8221; button in the right hand corner of the SmartSpaceConfig.</li>
		<li>Some residents with CI may take off their tag (thrown away by mistake) when they forget about their participation in the project. If so, remind the resident of the project, ask if they wish to continue, and where applicable, replace the wrist tag. In meetings with health care staff remind the stakeholders to talk with residents and remind them of their participation in the project.</li>
	</ol>
	</li>
	<li>Daily check that no wrist/hang tags have been submerged or otherwise damaed by&#160;water (resident takes a bath instead of shower); if water damage if visible, replace the tag.</li>
</ol>
4. Measuring Cognitive Impairment, Gait and Balance
<ol>
	<li>Register, download and assess the cognitive status of residents consenting to participate in the study at baseline and every 6 months over the course of the study using the Montreal Cognitive Assessment (MoCA).18
	<ol>
		<li>Input resident MoCA scores in a dataset which can be merged with the RTLS data through a data management program.</li>
	</ol>
	</li>
	<li>Recode raw MocA scores such that a MoCA score of &#8805;24 indicates no CI, a score ranging between 10-23 indicates mild/moderate CI, and a score ranging between 0-9 indicates severe CI.19</li>
	<li>Using the Tinetti Performance Oriented Mobility Assessment (POMA) and associated description,20assess the gait and balance of residents consenting to participate in the study each week over the course of the study period.20 
	NOTE: There are two subscales in the POMA with gait quality ranging from 0-12 and balance ability ranging from 0-16. Higher scores suggest fewer gait and balance impairments. These subscales gauge a variety of associated abilities and include tasks such as getting up from a chair, sitting and standing balance, balance while turning,step length, step height, deviation from a pathand stance. Frail or institutionalized older adults, consistent with the population utilized in this project, have a mean score of 11-12 (SD=3.3-5.7) on the balance ability subscale and a mean score of 8.1-8.6 (SD=3.2-4.6) on the gait quality subscale.1,21
	<ol>
		<li>Input gait and balance subscale and total scores in the database with other variables along with demographic characteristics of interest (age, race/ethnicity, gender).</li>
	</ol>
	</li>
	<li>Analyze the relationship between CI, gait, balance and ambulation activity in a data management/analysis program. Click crosstabs and input variables to examine bivariate relationships. Click chi square to examine the strength of the association between these variables of interest.</li>
</ol>

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<li>Bowen, M. E., Craighead, J., Wingrave, C. A., Kearns, W. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Real-Time+Locating+Systems+%28RTLS%29+to+Improve+Fall+Detection%5BTitle%5D)+AND+%22International+Journal+of+Gerontechnology%22%5BJournal%5D)">Real-Time Locating Systems (RTLS) to Improve Fall Detection.</a> International Journal of Gerontechnology. 9 (4), 464-471 (2010).</li>
<li>Kearns, W. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Path+tortuosity+in+everyday+movements+of+elderly+persons+increases+fall+prediction+beyond+knowledge+of+fall+history%2C+medication+use%2C+and+standardized+gait+and+balance+assessments%5BTitle%5D)+AND+%22J+Am+Med+Dir+Assoc%22%5BJournal%5D)">Path tortuosity in everyday movements of elderly persons increases fall prediction beyond knowledge of fall history, medication use, and standardized gait and balance assessments.</a> J Am Med Dir Assoc. 13 (7), e667-e665 (2012).</li>
<li>Bowen, M. E., Craighead, J. D., Klanchar, S. A., Nieves-Garcia, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multidrug-resistant+organisms+in+a+community+living+facility%3A+tracking+patient+interactions+and+time+spent+in+common+areas%5BTitle%5D)+AND+%22Am+J+Infect+Control%22%5BJournal%5D)">Multidrug-resistant organisms in a community living facility: tracking patient interactions and time spent in common areas.</a> Am J Infect Control. 40 (7), 677-679 (2012).</li>
</ol>

The authors have nothing to disclose.

There are several critical steps to be followed prior to beginning the RTLS project that are worth discussion. While a typical common area in a long-term care facility (about 10m x 13m or 1,000 square feet) requires four sensors, this varies based on the environment and the number of sensors required for the project are based on the level of precision required and the environment. Protrusions and glass walls, for example, will require additional sensors. If there are no line of sight issues, four sensors will cover an ev...

An older adult&#39;s ability to perform daily activities of daily living and be physically active is associated with gait quality and balance ability.1 Previous work shows correlations between balance ability and self-reported physical activity among sedentary older adults.2 These correlations remain across older adult populations. For example, among older adults in the community, self-reported activity levels are significantly correlated with balance3 and walking capacity;4 the physical activity of ambulatory long-term care residents is positively correlated with both gait and balance (using the Tinetti Performance Oriented Mobility Assessment).5 Institutionalization is associated with decreased walking activity in later life6 and result in a high prevalence of sedentary behavior in this population.7 In fact, a reported 80% or more of the waking hours of an institutionalized resident is spent sitting or lying down5 and few long-term care residents achieve the recommended 30 minutes of daily moderate activity.7 Inadequate physical activity is associated with de-conditioning, hospitalization and other poor health outcomes in this population. Understanding the walking activity of this population may aid in tailored gait and/or balance interventions to increase physical activity.
Some institutionalized older adults with cognitive impairment (CI) begin walking excessively as a result of disease progression. Wandering occurs when there are little/no breaks in activity over the course of several hours/days. Wandering is associated with fatigue, weight loss, injurious falls, sleep disturbances, getting lost, and death.8 Compared to nursing home residents with no or mild/moderate CI, residents with severe CI demonstrate 20% more activity characterized as wandering, 26% of which are &#34;lapping&#34; behaviors, a type of wandering where a resident circles the room.9 Despite this, it is difficult for health care staff and other observers to distinguish between physical activity and wandering. Intra-individual changes in walking activity can be subtle and wandering is not a behavioral problem to be curbed until the older adult attempts to elope (e.g., escape the facility). Wandering is common; the prevalence of wandering varies from study to study but an estimated 38%10 to 80% of older adults with CI will wander at some point over the course of the disease.11
It is difficult to understand the walking activity of institutionalized older adults as the population is heterogeneous (e.g., varying cognitive levels, health conditions) and activity is difficult to objectively measure. Self-report methods of activity by health care staff better reflect elopement or attempted escapes from the facility, and continuous observation over the long-term is vulnerable to inter-rater errors, time-consuming and expensive.12,13 Real-time locating system (RTLS) technologies have the potential to objectively and continuously measure walking activity among older adults with CI. Notably, there is heterogeneity in the RTLS field and multiple systems may theoretically be used: ultra-wideband (UWB; see attached Table of Materials), infrared + radio frequency, ultrasound and machine vision systems. However, to assess wandering behaviors, a tracking technology that is small and unobtrusive, wireless, capable of wide-area tracking, with no line of sight issues and accuracy to within 20cm is needed and there are few (if any) systems other than a RTLS using UWB that fulfills these requirements. For example, infrared + radio frequency technology rely on creating &#34;zones&#34; which detail when a resident passes through, but is not specific enough to determine wandering behaviors except within a meter or two, which is far too gross for these purposes. Ultrasound and machine vision have issues with identification and reflections; machine vision systems have good resolution but cannot differentiate residents without resorting to using an RFID tag to compensate for the inadequate capabilities of current artificial intelligence. A RTLS utilizing UWB has a wider range and spatial resolution of about 20cm -- versus one meter or more for other systems -- making it the most precise and capable of capturing all activity patterns.14,15 The RTLS using UWB discussed here is also stable, having been designed for 24/7 industrial applications. Researchers and clinicians have previously used this system where precision is essential - to prevent and predict falls, to assess dementia and changes in cognition - in a wide variety of settings -- assisted living, hospital, nursing homes, and rehabilitation units.13,16,17
This paper will detail the protocol of a RTLS using UWB to measure walking activity [walking distance, sustained walking distance, and sustained gait speed (average meters per second/week calculated during sustained walking only)] and paper and pencil tests of CI, gait ability and balance quality, as the latter of which are key components of walking activity. Study findings will focus on using RTLS to distinguish between walking distance, which is associated with physical activity and thus positive health outcomes, and sustained walking distance which is associated with wandering and thus negative health outcomes.

<table><tbody><tr><td>UWB Sensor</td><td>Ubisense</td><td></td><td>There are two product lines to choose from; IP30 is the latest</td></tr><tr><td>Tags</td><td>Ubisense</td><td></td><td>There are two types of tags to choose from; if IP30 sensors are chosen, use DFLAT33 mini tags</td></tr><tr><td>Timing Distribution Unit</td><td>Ubisense</td><td>UBITIMING</td><td></td></tr><tr><td>Network and Timing Combiner</td><td>Ubisense</td><td>UBICOMSPL21</td><td></td></tr><tr><td>Home Base License</td><td>Ubisense</td><td>HOMEBASE</td><td></td></tr><tr><td>Expert Support</td><td>Ubisense</td><td>MANDS2</td><td></td></tr><tr><td>Project Implmentation Services</td><td>Ubisense</td><td>PROJSERV</td><td></td></tr><tr><td>Smart Factory</td><td>Ubisense&amp;nbsp;</td><td></td><td>specialized software designed to manage the RTLS</td></tr><tr><td>Server</td><td>Any</td><td></td><td>Laptop with at least 8MB RAM</td></tr><tr><td>Network Cabling</td><td>Any</td><td></td><td>3rd party or subcontract&amp;nbsp;</td></tr><tr><td>Tinetti Performance Oriented Mobility Assessment</td><td></td><td></td><td>Tinetti ME, Williams TF, Mayewski R. Fall risk index for elderly patients based on number of chronic disabilities. The American journal of medicine. Mar 1986;80(3):429-434</td></tr><tr><td>The Montreal Cognitive Assessment</td><td></td><td></td><td>https://www.mocatest.org</td></tr></tbody></table>

using a real-time locating system to measure walking activity associated with wandering behaviors among institutionalized older adults

A real-time locating system (RTLS) can be used to track the walking activity of institutionalized older adults in long-term care who are at risk for wandering behaviors. The benefits of a RTLS are objective and continuous measurements of activity. Self-report methods of activity, especially wandering, by health care staff are vulnerable to floor effects and recall bias, and continuous clinical or research observation over the long-term can be time-consuming and expensive. Health care staff also fail to recognize the onset and/or duration of wandering behaviors, which are associated with a variety of adverse health outcomes in this population but amenable to intervention. RTLS technologies can measure the walking activity of institutionalized residents with cognitive impairment over time with a high degree of accuracy. This is particularly useful for the study of wandering, defined as walking for at least 60 seconds with few (if any) breaks in activity. Wandering is associated with disease progression, hospitalizations, falls and death. Previous work suggests older adults with poor balance ability and high sustained walking activity may be particularly susceptible to poor health outcomes. RTLS&#39;s are used to assess cognitive impairment and factors associated with gait and balance; however, supplemental paper and pencil gait/balance tools may be used to further refine risk profiles. This project discusses the use of a RTLS to measure walking activity and also gait quality and balance ability measures on this population.

RTLS raw data require smoothing to improve the location data&#39;s precision (see protocol step 9 under the section, &#34;Use the RTLS Tags to Locate and Track Residents in Real-Time&#34;). Though controlled with default settings in the power plot tab during installation and set-up (see step 1.6.3 in the associated protocol), without additional smoothing there will continue to be noise and jumps. With regard to noise, even when sedentary for several hours, the active RTLS tag continues to...

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Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

This paper discusses the use of a continuous and objective real-time locating system to measure walking activity associated with wandering behaviors, focusing on older adults with cognitive impairment. Walking activity is measured by walking distance, sustained walking distance, and sustained gait speed. Also assessed are gait quality and balance ability.

A real-time locating system (RTLS) can be used to track the walking activity of institutionalized older adults in long-term care who are at risk for ...

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Research

JoVE Journal

Behavior

6.7K Views.  University of Delaware. Personeel in de gezondheidszorg kan geen onderscheid maken tussen lichaamsbeweging en zwerven, en alleen identificeren zwerven wanneer een patiënt het risico loopt de faciliteit te verlaten. Veranderingen in de loopactiviteit worden geassocieerd met cognitieve stoornissen, vallen, zwerven, en andere slechte gezondheidsresultaten. Het real-time lokalisatiesysteem houdt continu en objectief de loopactiviteit bij.Dit omvat gunstige fysieke activiteit en zwerven. Dwalen is patroon lopen d...

Video: Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

In trace fear conditioning a conditional stimulus (CS) predicts the occurrence of the unconditional stimulus (UCS), which is presented after a brief stimulus free period (trace interval)1. Because the CS and UCS do not co-occur temporally, the subject must maintain a representation of that CS during the trace interval. In humans, this type of learning requires awareness of the stimulus contingencies in order to bridge the trace interval2-4. However when a face is used as a CS, subjects can implicitly learn to fear the face even in the absence of explicit awareness*. This suggests that there may be additional neural mechanisms capable of maintaining certain types of "biologically-relevant" stimuli during a brief trace interval. Given that the amygdala is involved in trace conditioning, and is sensitive to faces, it is possible that this structure can maintain a representation of a face CS during a brief trace interval.
It is challenging to understand how the brain can associate an unperceived face with an aversive outcome, even though the two stimuli are separated in time. Furthermore investigations of this phenomenon are made difficult by two specific challenges. First, it is difficult to manipulate the subject's awareness of the visual stimuli. One common way to manipulate visual awareness is to use backward masking. In backward masking, a target stimulus is briefly presented (&lt; 30 msec) and immediately followed by a presentation of an overlapping masking stimulus5. The presentation of the mask renders the target invisible6-8. Second, masking requires very rapid and precise timing making it difficult to investigate neural responses evoked by masked stimuli using many common approaches. Blood-oxygenation level dependent (BOLD) responses resolve at a timescale too slow for this type of methodology, and real time recording techniques like electroencephalography (EEG) and magnetoencephalography (MEG) have difficulties recovering signal from deep sources.
However, there have been recent advances in the methods used to localize the neural sources of the MEG signal9-11. By collecting high-resolution MRI images of the subject's brain, it is possible to create a source model based on individual neural anatomy. Using this model to "image" the sources of the MEG signal, it is possible to recover signal from deep subcortical structures, like the amygdala and the hippocampus*.

This article describes how to record amygdala activity with magnetoencephalography (MEG). In addition this article will describe how to conduct trace fear conditioning without awareness, a task that activates the amygdala. It will cover 3 topics: 1) Designing a trace conditioning paradigm using backward masking to manipulate awareness. 2) Recording brain activity during the task using magnetoencephalography. 3) Using source imaging to recover signal from subcortical structures.

In trace fear conditioning a conditional  stimulus (CS) predicts the occurrence of the unconditional stimulus (UCS),  which is presented after a brief ...

Community-based Adapted Tango Dancing for Individuals with Parkinson's Disease and Older Adults

Adapted tango dancing improves mobility and balance in older adults and additional populations with balance impairments. It is composed of very simple step elements. Adapted tango involves movement initiation and cessation, multi-directional perturbations, varied speeds and rhythms. Focus on foot placement, whole body coordination, and attention to partner, path of movement, and aesthetics likely underlie adapted tango&#8217;s demonstrated efficacy for improving mobility and balance. In this paper, we describe the methodology to disseminate the adapted tango teaching methods to dance instructor trainees and to implement the adapted tango by the trainees in the community for older adults and individuals with Parkinson&#8217;s Disease (PD). Efficacy in improving mobility (measured with the Timed Up and Go, Tandem stance, Berg Balance Scale, Gait Speed and 30 sec chair stand), safety and fidelity of the program is maximized through targeted instructor and volunteer training and a structured detailed syllabus outlining class practices and progression.

Adapted tango has demonstrated efficacy for improving mobility and balance. We describe the dissemination of adapted tango teaching methods to dance instructor trainees and the implementation of adapted tango by the trainees in the community for improving mobility and balance in older adults and individuals with Parkinson&#8217;s disease.

Adapted tango dancing improves mobility and balance in older adults and additional populations with balance impairments. It is composed of very simple ...

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 ...

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 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

Walk with Me Hybrid Virtual/In-Person Walking for Older Adults with Neurodegenerative Disease

This study presents a protocol for virtual and in-person walking groups for older adults with neurodegenerative diseases and addresses the decline in physical activity and social connectivity during the pandemic. Physical activity, specifically moderate-intensity walking, has been shown to have multiple health benefits for older adults. This methodology was created during the COVID-19 pandemic, contributing to lower physical activity levels and social isolation in older adults. Both the in-person and virtual classes utilize technology such as fitness tracking apps and video platforms. Data are presented from two groups of older adults with neurodegenerative disease: people with prodromal Alzheimer's disease and people with Parkinson's disease. Participants in the virtual classes were screened for balance impairments before the walk, and any person deemed at risk for falls was not eligible to participate virtually. As COVID vaccines became available and restrictions were lifted, in-person walking groups became feasible. Staff and caregivers were trained in balance management, roles/responsibilities, and delivering walking cues. Both walks, virtual and in-person, consisted of a warm up, a walk, and a cool down, and posture, gait, and safety cues were given throughout. Measures of the rate of perceived exertion (RPE) and heart rate (HR) were taken pre-warm up, post-warm up, and at 15 min, 30 min, and 45 min time points. Participants also used a walking application (app) on their phones to record the distance walked and the number of steps taken. The study showed a positive correlation between HR and RPE in both groups. The participants in the virtual group rated the walking group favorably in terms of improving their quality of life during a period of social isolation and contributing to physical, mental, and emotional well-being. The methodology shows a safe and feasible way to implement virtual and in-person walking groups for older adults with neurological diseases.

The present protocol aims to inform rehabilitation specialists and fitness instructors in safe, feasible, and evidence-based methods of delivering virtual and in-person walking classes to older adults with neurodegenerative diseases.

This study presents a protocol for virtual and in-person walking groups for older adults with neurodegenerative diseases and addresses the decline in ...

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

A decline in the performance of instrumental activities of daily living (IADLs) has been proposed as a prodromal marker of neurological disease. Existing clinical and performance-based IADL assessments are not feasible for integration into clinical medicine. Virtual reality (VR) is a powerful yet underutilized tool that could advance the diagnosis and treatment of neurological disease. An impediment to the adoption and scaling of VR in clinical neurology is VR-related sickness resulting from sensory inconsistencies between the visual and vestibular systems (i.e., locomotion problem).
The Cleveland Clinic Virtual Reality Shopping (CC-VRS) platform attempts to solve the locomotion problem by coupling an omnidirectional treadmill with high-resolution VR content, enabling the user to physically navigate a virtual grocery store to simulate shopping. The CC-VRS consists of Basic and Complex shopping experiences; both require walking 150 m and retrieving five items. The Complex experience has additional scenarios that increase the cognitive and motor demands of the task to better represent the continuum of activities associated with real-world shopping. The CC-VRS platform provides objective and quantitative biomechanical and cognitive outcomes related to the user&#39;s IADL performance. Initial data indicate that the CC-VRS results in minimal VR-sickness and is feasible and tolerable for older adults and patients with Parkinson&#39;s disease (PD). The considerations underlying the development, design, and hardware and software technology are reviewed, and initial models of integration into primary care and neurology are provided.

Virtual reality (VR) is a powerful yet underutilized approach to advance the diagnosis and treatment of neurological disease. The Cleveland Clinic Virtual Reality Shopping platform combines state-of-the-art VR content with an omnidirectional treadmill to quantify instrumental activities of daily living-a proposed prodromal marker of neurological disease.

A decline in the performance of instrumental activities of daily living (IADLs) has been proposed as a prodromal marker of neurological disease. Existing ...

Engineering

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.

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 ...

Bioengineering

Traditional Trail Making Test Modified into Brand-new Assessment Tools: Digital and Walking Trail Making Test

The Trail Making Test (TMT) is a well-accepted tool for evaluating executive function. The standard TMT was invented more than 60 years ago and has been modified into many versions. With the development of digital technologies, TMT is now modified to a digitized version. The present study demonstrated digital TMT (dTMT) performed on a computer, and Walking TMT (WTMT) on the floor. Both revealed more information compared with the traditional version of TMT.

Here, we present a protocol to show how to perform two types of cognitive assessment tools derived from the paper-pencil version of the Trail Making Test.

The Trail Making Test (TMT) is a well-accepted tool for evaluating executive function. The standard TMT was invented more than 60 years ago and has been ...

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

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