Wearable inertial sensors can be used to measure movements in everyday life. Advanced algorithms translate these movements into metrics that can be interpreted by clinicians, patients and consumers. We believe that these wearables are the future for movement assessment and can empower patients towards taking control of their own health. They are relatively cheap, small, can be worn over longer periods of time, are easy to handle, and collect data objectively and unobtrusively. Especially in progressive movement disorders, such as Parkinson’s disease, it is important to monitor the course of the disease and the effects of therapy.

We were interested in whether wearing wearables over a longer time period would affect Health-Related Quality of Life in people with Parkinson’s disease. A 12-week study over three centres in Europe (Tromsø, Lisbon and Tübingen) included 22 people with Parkinson disease. Half of these people received a sensor set with three wearables for the day and one wearable during the night; the other half did not receive a sensor set. During the first four weeks the sensor group did not receive feedback on their movement behaviour. In the following eight weeks, the sensor group received feedback on their movement behaviour. Health-Related Quality of Life was assessed after four, twelve and fourteen weeks (follow-up). We assessed: i) overall Health-Related Quality of Life, ii) Health-Related Quality of Life in the mobility domain, and iii) Health-Related Quality of Life in the activities of daily living domain. After the first four weeks, no significant changes in Health-Related Quality of Life between groups could be identified. After twelve weeks, a tendency towards an improved Health-Related Quality of Life in the mobility domain was seen in the sensor group compared to the non-sensor group. After fourteen weeks, a significantly improved Health-Related Quality of Life in the mobility domain was detected in the sensor group.

These findings indicate a high acceptance of wearable sensor systems by people with Parkinson’s disease, even over longer periods of time. Under certain circumstances (e.g., when providing useful feedback about their movement behaviour) wearables may even have a positive effect on (aspects of) Health-Related Quality of Life. This may be best explained by a positive effect of increased self-knowledge. Self-knowledge can enable patients to actively take part in the decision-making process during treatment and increase self-empowerment, eventually leading to better teamwork between physician and patient, better health outcomes, and increased patient satisfaction.

Publication

van Uem JMT, Maier KS, Hucker S, Scheck O, Hobert MA, Santos AT, Fagerbakke Ø, Larsen F, Ferreira JJ, Maetzler W. Twelve-Week Sensor Assessment in Parkinson’s Disease: Impact on Quality of Life. Mov Disord. 2016; May 31. doi:10.1002/mds.26676.

About the Author

Falls are the leading cause of injury-related morbidity and mortality among older adults. In addition to fractures and traumatic brain injuries, ‘long lie’ is another serious consequence of falls in this population. Long lies are experienced by individuals who are unable to get up after a fall and remain on the ground for an extended period of time. Wearable sensor systems that can automatically detect falls and send an alert to care providers can reduce the frequency and severity of long lies. Ideally speaking, such fall detection systems/algorithms should be developed using real-life fall and activity data acquired directly from older adults as they go about their daily activities. However, recording falls through this approach could prove to be extremely time-consuming, spanning several years of strenuous data collection from a large population. While these efforts should be continued, designing and developing a comprehensive laboratory-based fall experiment representing a range of real-life falls and related activities in older adults can serve as an alternative to develop such fall detection algorithms. These algorithms can later be validated and easily adjusted on real-life data.

To design and carry out such a laboratory experiment with young adults, we first studied video footage of 227 falls experienced by 130 older residents of a local long-term care facility. Based on this footage, 86% of the falls were collectively due to (i) incorrect shift of bodyweight (41 %), (ii) tripping (21 %), (iii) hit/bump (11 %), (iv) collapse/loss of consciousness (10 %), and (v) slipping (3 %). Our experimental design included these falls, along with a range of near-falls and activities of daily living (ADLs). Near-falls are particularly important to be included in experiments in order to limit the occurrence of false positives.

Figure: Signals acquired from a waist-mounted tri-axial accelerometer from a typical participant during: A) falls, B) near-falls, and C) activities of daily living (ADLs). AP, ML and Inf/Sup denote acceleration in anterior/posterior, medial/lateral and inferior/superior directions.

On the aforementioned dataset, we tested the accuracy of five previously published threshold-based algorithms and five novel machine learning fall detection algorithms to differentiate falls from near-falls and ADLs. Our results show that the machine learning algorithms provided greater overall sensitivity and specificity than the threshold-based algorithms. Among the machine learning algorithms, Support Vector Machines provided the greatest sensitivity and specificity of 96% in distinguishing falls from non-falls. Our results provide a template for further improvement in designing a robust fall detection system, which is necessary for the development of ‘smart’ personal emergency response system that can automatically place a call for help in case of a fall to prevent long lies.

Publication

Aziz O, Musngi M, Park EJ, Mori G, Robinovitch SN. A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Med Biol Eng Comput. 2016 Apr 22. [Epub ahead of print] (http://www.ncbi.nlm.nih.gov/pubmed/27106749)

About the Author

The anchor system (AS) is a relatively new and efficient tool used to provide additional haptic information to the postural control system. It requires a person to hold, in each hand, a flexible cable with a light load attached at the end, which rests on the floor.  While standing or walking, the person pulls on the cable just enough to keep it taut. The AS provides haptic information on the spatial orientation of the body with respect to the floor through changes in cable tension. The AS holds promise for balance training programs because it allows flexibility of use, which may not be the case for the commonly used light touch paradigm. Prior studies showed that older adults could utilize the AS to reduce body sway during standing. The potential benefit of the AS for walking stability was still unknown, which is why we performed this study.

44 older adults participated in this study on the immediate and short-term training effect of the AS on body sway during walking. Participants were randomized into three groups: control, 0g-anchor, and 125g-anchor. Participants were asked to perform five blocks of three trials each. During all trials, participants performed 5-m tandem walking with an accelerometer attached to their back. The first and last blocks were considered pre- and post-tests, where participants walked without the AS. The three training blocks in-between were unique to each group. The control group did not use the AS. The 125g-anchor group used the regular AS and participants dragged the anchors as they walked. The 0g-anchor group held the anchor’s cable without any load at the end to prevent any additional haptic information. Our results show that the RMS of the trunk acceleration (a measure of trunk stability) did not differ among groups in the pre- and post-tests. However, the anchor-125g group reduced their RMS trunk acceleration during the training compared to the control group.

The reduction in RMS trunk acceleration with the AS opens the possibility of incorporating it into balance training programs. Although there was no short-term training effect due to anchor training, the amount of practice may have been small (only nine trials) and future studies should investigate a large amount of practice with the AS. People with balance problems can directly benefit from the use of the AS in daily life as it can be used while walking under varying task demands (e.g., changes in direction and uneven terrain).

Publication

Costa AA, Manciopi PA, Mauerberg-deCastro E, Moraes R. Haptic information provided by the “anchor system” reduces trunk sway acceleration in the frontal plane during tandem walking in older adults. Neurosci Lett. 2015;609:1-6. doi: 10.1016/j.neulet.2015.10.004.

http://www.sciencedirect.com/science/article/pii/S0304394015301671

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An accurate assessment of fall risk could assist to identify older people at increased risk and would enable appropriate intervention strategies before a fall occurs. Recent technological advances hold promise for more accurate and objective assessments. But, how can we achieve regular and cost-efficient assessments? One solution could be to develop self-assessment approaches that empower people to take an active role in their own health management.

We developed a Kinect-based system, which can be connected to a normal TV, for the self-assessment of fall risk at home. Various tests of balance, strength and reaction time were implemented into the assessment software (Figure). In our publication in the Journal Gerontology, we focused on the Kinect-based five-times-sit-to-stand implementation (Figure A) and investigated the feasibility and validity of this test to identify older people at increased risk of falling.

Figure: Participant performing the Kinect-based (A) five-times-sit-to-stand, (B) balance, (C) stepping reaction time, (D) reaching reaction time test in front of a TV.

We validated the Kinect-based five-times-sit-to-stand test with 94 community-dwelling older people aged 65 years or older in the laboratory. Afterwards, we installed the system into the participants’ homes. We developed a signal processing algorithm to extract fall risk-related measurements (temporal and spatial) from the Kinect sensor data and then validated the derived performance measurements by comparing the results of previous fallers and non-fallers.

Fallers performed significantly worse than non-fallers on the Kinect-based five-times-sit-to-stand measurements. Participants were satisfied with this technological approach and performed the five-times-sit-to-stand test on a regular basis. The home-based measurements were significantly correlated to the laboratory measurements indicating that this test could be used to assess fall risk at home.

This study was an important first step towards the development of a novel self-assessment solution. An assessment conducted at home could have several advantages over a clinical assessment. It might be more convenient and would provide an easy access for people living in rural and remote areas with limited access to proper health care. Home-based assessments are conducted in a person’s natural environment which might lead to more accurate and representative results of a person’s real life performance. With further research, such assessment tests may prove useful as a measure for monitoring fall risk changes over time as well as the effects of fall prevention interventions.

Publication
A. Ejupi, M. Brodie, Y. J. Gschwind, S. R. Lord, W. L. Zagler, and K. Delbaere, “Kinect-Based Five-Times-Sit-to-Stand Test for Clinical and In-Home Assessment of Fall Risk in Older People.” Gerontology, vol. 62, no. 1, 2016
https://www.karger.com/Article/FullText/381804

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