How wearable sensors have been utilised to evaluate frailty in older adults: a systematic review.

BACKGROUND: Globally the population of older adults is increasing. It is estimated that by 2050 the number of adults over the age of 60 will represent over 21% of the world's population. Frailty is a clinical condition associated with ageing resulting in an increase in adverse outcomes. It is considered the greatest challenge facing an ageing population affecting an estimated 16% of community-dwelling populations worldwide. AIM: The aim of this systematic review is to explore how wearable sensors have been used to assess frailty in older adults. METHOD: Electronic databases Medline, Science Direct, Scopus, and CINAHL were systematically searched March 2020 and November 2020. A search constraint of articles published in English, between January 2010 and November 2020 was applied. Papers included were primary observational studies involving; older adults aged > 60 years, used a wearable sensor to provide quantitative measurements of physical activity (PA) or mobility and a measure of frailty. Studies were excluded if they used non-wearable sensors for outcome measurement or outlined an algorithm or application development exclusively. The methodological quality of the selected studies was assessed using the Appraisal Tool for Cross-sectional Studies (AXIS). RESULTS: Twenty-nine studies examining the use of wearable sensors to assess and discriminate between stages of frailty in older adults were included. Thirteen different body-worn sensors were used in eight different body-locations. Participants were community-dwelling older adults. Studies were performed in home, laboratory or hospital settings. Postural transitions, number of steps, percentage of time in PA and intensity of PA together were the most frequently measured parameters followed closely by gait speed. All but one study demonstrated an association between PA and level of frailty. All reports of gait speed indicate correlation with frailty. CONCLUSIONS: Wearable sensors have been successfully used to evaluate frailty in older adults. Further research is needed to identify a feasible, user-friendly device and body-location that can be used to identify signs of pre-frailty in community-dwelling older adults. This would facilitate early identification and targeted intervention to reduce the burden of frailty in an ageing population.

Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study.

BACKGROUND: Accurate assessments of adherence and exercise performance are required in order to ensure that patients adhere to and perform their rehabilitation exercises correctly within the home environment. Inertial sensors have previously been advocated as a means of achieving these requirements, by using them as an input to an exercise biofeedback system. This research sought to investigate whether inertial sensors, and in particular a single sensor, can accurately classify exercise performance in patients performing lower limb exercises for rehabilitation purposes. METHODS: Fifty-eight participants (19 male, 39 female, age: 53.9 ± 8.5 years, height: 1.69 ± 0.08 m, weight: 74.3 ± 13.0 kg) performed ten repetitions of seven lower limb exercises (hip abduction, hip flexion, hip extension, knee extension, heel slide, straight leg raise, and inner range quadriceps). Three inertial sensor units, secured to the thigh, shin and foot of the leg being exercised, were used to acquire data during each exercise. Machine learning classification methods were applied to quantify the acquired data. RESULTS: The classification methods achieved relatively high accuracy at distinguishing between correct and incorrect performance of an exercise using three, two, or one sensor while moderate efficacy scores were also achieved by the classifier when attempting to classify the particular error in exercise performance. Results also illustrated that a reduction in the number of inertial sensor units employed has little effect on the overall efficacy results. CONCLUSION: The results revealed that it is possible to classify lower limb exercise performance using inertial sensors with satisfactory levels of accuracy and reducing the number of sensors employed does not reduce the accuracy of the method.

Integrating the quantitative with the qualitative: findings from a mixed methods cardiac rehabilitation exercise trial.

Background: Cardiac rehabilitation is a core component of cardiovascular disease management. Eastern Corridor Medical Engineering-Cardiac Rehabilitation is a digital health platform for online cardiac rehabilitation exercise. We conducted a mixed methods pilot trial to evaluate Eastern Corridor Medical Engineering-Cardiac Rehabilitation. Objective: The study sought to examine the difference between objectively measured outcomes and participant perceptions of benefits and improvements gained from participation in a cardiac rehabilitation exercise program. Methods: Seventeen participants (14 male, 3 female; 69.5 ± 7.3 years of age) took part and were allocated to 1 of 2 groups; an online exercise group (n = 8), or an in-person exercise (n = 9) group. Due to the COVID-19 pandemic, a pragmatic approach to group allocation was adopted. Objective outcomes were assessed at baseline and repeated following the intervention period, with the primary outcome being 6-minute walk test distance. In addition to clinical outcome measurements, we undertook qualitative interviews with participants. Results: Only 5 participants demonstrated a clinically meaningful improvement in 6-minute walk test distance, following the 8-week exercise program. The main theme emerging from the qualitative interviews was the valued benefits of the cardiac rehabilitation exercise program. Despite the lack of measurable physical change, participants self-defined a range of benefits they valued and attributed directly to participation in the cardiac rehabilitation exercise program. Conclusion: The findings from this study may offer a useful starting point for further study of community-based cardiac rehabilitation exercise and also highlight the benefit of adopting a mixed methods approach that considers both the objective outcomes measured as well as the subjective reports obtained from participants.

Biofeedback in rehabilitation.

This paper reviews the literature relating to the biofeedback used in physical rehabilitation. The biofeedback methods used in rehabilitation are based on biomechanical measurements and measurements of the physiological systems of the body. The physiological systems of the body which can be measured to provide biofeedback are the neuromuscular system, the respiratory system and the cardiovascular system. Neuromuscular biofeedback methods include electromyography (EMG) biofeedback and real-time ultrasound imaging (RTUS) biofeedback. EMG biofeedback is the most widely investigated method of biofeedback and appears to be effective in the treatment of many musculoskeletal conditions and in post cardiovascular accident (CVA) rehabilitation. RTUS biofeedback has been demonstrated effective in the treatment of low back pain (LBP) and pelvic floor muscle dysfunction. Cardiovascular biofeedback methods have been shown to be effective in the treatment of a number of health conditions such as hypertension, heart failure, asthma, fibromyalgia and even psychological disorders however a systematic review in this field has yet to be conducted. Similarly, the number of large scale studies examining the use of respiratory biofeedback in rehabilitation is limited. Measurements of movement, postural control and force output can be made using a number of different devices and used to deliver biomechanical biofeedback. Inertial based sensing biofeedback is the most widely researched biomechanical biofeedback method, with a number of studies showing it to be effective in improving measures of balance in a number of populations. Other types of biomechanical biofeedback include force plate systems, electrogoniometry, pressure biofeedback and camera based systems however the evidence for these is limited. Biofeedback is generally delivered using visual displays, acoustic or haptic signals, however more recently virtual reality (VR) or exergaming technology have been used as biofeedback signals. VR and exergaming technology have been primarily investigated in post-CVA rehabilitation, however, more recent work has shown this type of biofeedback to be effective in improving exercise technique in musculoskeletal populations. While a number of studies in this area have been conducted, further large scale studies and reviews investigating different biofeedback applications in different clinical populations are required.

Waist-What? Can a single sensor positioned at the waist detect parameters of gait at a speed and distance reflective of older adults' activity?

One of the problems facing an ageing population is functional decline associated with reduced levels of physical activity (PA). Traditionally researcher or clinician input is necessary to capture parameters of gait or PA. Enabling older adults to monitor their activity independently could raise their awareness of their activitiy levels, promote self-care and potentially mitigate the risks associated with ageing. The ankle is accepted as the optimum position for sensor placement to capture parameters of gait however, the waist is proposed as a more accessible body-location for older adults. This study aimed to compare step-count measurements obtained from a single inertial sensor positioned at the ankle and at the waist to that of a criterion measure of step-count, and to compare gait parameters obtained from the sensors positioned at the two different body-locations. Step-count from the waist-mounted inertial sensor was compared with that from the ankle-mounted sensor, and with a criterion measure of direct observation in healthy young and healthy older adults during a three-minute treadmill walk test. Parameters of gait obtained from the sensors at both body-locations were also compared. Results indicated there was a strong positive correlation between step-count measured by both the ankle and waist sensors and the criterion measure, and between ankle and waist sensor step-count, mean step time and mean stride time (r = .802-1.0). There was a moderate correlation between the step time variability measures at the waist and ankle (r = .405). This study demonstrates that a single sensor positioned at the waist is an appropriate method for the capture of important measures of gait and physical activity among older adults.

Remotely Delivered Cardiac Rehabilitation Exercise for Coronary Heart Disease: Nonrandomized Feasibility Study.

BACKGROUND: Exercise-based cardiac rehabilitation (CR) is recommended for coronary heart disease (CHD). However, poor uptake of and poor adherence to CR exercise programs have been reported globally. Delivering CR exercise classes remotely may remove some of the barriers associated with traditional hospital- or center-based CR. OBJECTIVE: We have developed a bespoke platform, Eastern Corridor Medical Engineering Centre-Cardiac Rehabilitation (ECME-CR), to support remotely delivered CR exercise. This pilot trial sought to test the ECME-CR platform and examine the efficacy and feasibility of a remote CR exercise program compared to a traditional center-based program. METHODS: In all, 21 participants with CHD were recruited and assigned to either the intervention or control group. Both groups performed the same 8-week exercise program. Participants in the intervention group took part in web-based exercise classes and used the ECME-CR platform during the intervention period, whereas participants in the control group attended in-person classes. Outcomes were assessed at baseline and following the 8-week intervention period. The primary outcome measure was exercise capacity, assessed using a 6-minute walk test (6MWT). Secondary outcomes included measurement of grip strength, self-reported quality of life, heart rate, blood pressure, and body composition. A series of mixed between-within subjects ANOVA were conducted to examine the mean differences in study outcomes between and within groups. Participant adherence to the exercise program was also analyzed. RESULTS: In all, 8 participants (male: n=5; age: mean 69.7, SD 7.2 years; height: mean 163.9, SD 5.4 cm; weight: mean 81.6, SD 14.1 kg) in the intervention group and 9 participants (male: n=9; age: mean 69.8, SD 8.2 years; height: mean 173.8, SD 5.2 cm; weight: mean 94.4, SD 18.0 kg) in the control group completed the exercise program. Although improvements in 6MWT distance were observed from baseline to follow-up in both the intervention (mean 490.1, SD 80.2 m to mean 504.5, SD 93.7 m) and control (mean 510.2, SD 48.3 m to mean 520.6, SD 49.4 m) group, no significant interaction effect (F1,14=.026; P=.87) nor effect for time (F1,14=2.51; P=.14) were observed. No significant effects emerged for any of the other secondary end points (all P>.0275). Adherence to the exercise program was high in both the intervention (14.25/16, 89.1%) and control (14.33/16, 89.6%) group. No adverse events or safety issues were reported in either group during the study. CONCLUSIONS: This pilot trial did not show evidence of significant positive effect for either the remotely delivered or center-based program. The 6MWT may not have been sufficiently sensitive to identify a change in this cohort of participants with stable CHD. This trial does provide evidence that remote CR exercise, supported with digital self-monitoring, is feasible and may be considered for individuals less likely to participate in traditional center-based programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/31855.

Investigating the needs and concerns of older adults with multimorbidity and their healthcare professionals for conceivable digital psychotherapeutic interventions.

Multimorbidity, defined as the concurrent experience of more than one chronic health condition in an individual, affects ∼65% of people over 65 and 85% of those over 85 years old with 30% of those also experiencing mental health concerns. This can lead to reduced quality of life and functioning as well as poorer outcomes in terms of condition management, adherence to treatment, and ultimately disease prognosis and progression. Digital health interventions offer a viable means of condition self-management, as well as psychological support, particularly for those who may have difficulty accessing in-person services. To best meet the needs of older adults with multimorbidity, deeper insights are needed into their specific concerns and issues around condition management, particularly with regard to distress in relation to managing one's condition. The present study aimed to explore this using one-to-one qualitative interviews and focus groups with people with chronic health conditions and healthcare professionals. Participants were 11 older adults with multimorbidity (4 males; mean age: M = 72.7 years) and 14 healthcare professionals including five clinical nurse specialists, four pharmacists, two general practitioners, one occupational therapist, one speech and language therapist and one dietician. Thematic analysis was used to identify key themes, which included: patient feelings of anxiety or worry leading to an unwillingness to access essential information; the various mental health challenges faced by those with multimorbidity; the importance of personal values in providing motivation; and the importance of social support. Findings are discussed in relation to the potential development of transdiagnostically applicable digital interventions for the management of distress in those with multimorbidity.

Measurement of Heart Rate Using the Withings ScanWatch Device During Free-living Activities: Validation Study.

BACKGROUND: Wrist-worn devices that incorporate photoplethysmography (PPG) sensing represent an exciting means of measuring heart rate (HR). A number of studies have evaluated the accuracy of HR measurements produced by these devices in controlled laboratory environments. However, it is also important to establish the accuracy of measurements produced by these devices outside the laboratory, in real-world, consumer use conditions. OBJECTIVE: This study sought to examine the accuracy of HR measurements produced by the Withings ScanWatch during free-living activities. METHODS: A sample of convenience of 7 participants volunteered (3 male and 4 female; mean age 64, SD 10 years; mean height 164, SD 4 cm; mean weight 77, SD 16 kg) to take part in this real-world validation study. Participants were instructed to wear the ScanWatch for a 12-hour period on their nondominant wrist as they went about their day-to-day activities. A Polar H10 heart rate sensor was used as the criterion measure of HR. Participants used a study diary to document activities undertaken during the 12-hour study period. These activities were classified according to the 11 following domains: desk work, eat or drink, exercise, gardening, household activities, self-care, shopping, sitting, sleep, travel, and walking. Validity was assessed using the Bland-Altman analysis, concordance correlation coefficient (CCC), and mean absolute percentage error (MAPE). RESULTS: Across all activity domains, the ScanWatch measured HR with MAPE values <10%, except for the shopping activity domain (MAPE=10.8%). The activity domains that were more sedentary in nature (eg, desk work, eat or drink, and sitting) produced the most accurate HR measurements with a small mean bias and MAPE values <5%. Moderate to strong correlations (CCC=0.526-0.783) were observed between devices for all activity domains, except during the walking activity domain, which demonstrated a weak correlation (CCC=0.164) between devices. CONCLUSIONS: The results of this study show that the ScanWatch measures HR with a degree of accuracy that is acceptable for general consumer use; however, it would not be suitable in circumstances where more accurate measurements of HR are required, such as in health care or in clinical trials. Overall, the ScanWatch was less accurate at measuring HR during ambulatory activities (eg, walking, gardening, and household activities) compared to more sedentary activities (eg, desk work, eat or drink, and sitting). Further larger-scale studies examining this device in different populations and during different activities are required.

Does Connected Health Technology Improve Health-Related Outcomes in Rural Cardiac Populations? Systematic Review Narrative Synthesis.

Individuals living in rural areas are more likely to experience cardiovascular diseases (CVD) and have increased barriers to regular physical activity in comparison to those in urban areas. This systematic review aimed to understand the types and effects of home-based connected health technologies, used by individuals living in rural areas with CVD. The inclusion criteria included technology deployed at the participant's home and could be an mHealth (smart device, fitness tracker or app) or telehealth intervention. Nine electronic databases were searched across the date range January 1990-June 2021. A total of 207 full texts were screened, of which five studies were included, consisting of 603 participants. Of the five studies, four used a telehealth intervention and one used a form of wearable technology. All interventions which used a form of telehealth found a reduction in overall healthcare utilisation, and one study found improvements in CVD risk factors. Acceptability of the technologies was mixed, in some studies barriers and challenges were cited. Based on the findings, there is great potential for implementing connected health technologies, but due to the low number of studies which met the inclusion criteria, further research is required within rural areas for those living with cardiovascular disease.

Efficacy of a Digital Acceptance and Commitment Therapy Intervention for the Improvement of Self-management Behaviors and Psychological Flexibility in Adults With Cardiac Disease: Protocol for a Single Case Experimental Design.

BACKGROUND: Research indicates that the management of distress levels in those with cardiac disease is not only important for improving quality of life and functioning but also critical for condition management; adherence to treatment; and, ultimately, disease prognosis and progression. Acceptance and commitment therapy (ACT) has consistently demonstrated positive long-term outcomes across a wide array of conditions, including chronic illness. However, most empirical investigations conducted to date have also involved in-person therapy, which can be difficult to access, particularly for those dealing with the demands of chronic disease. OBJECTIVE: The objective of our research is to evaluate a digital ACT intervention for improving self-management behaviors and distress levels in those with cardiac conditions. METHODS: The digital ACT intervention will be delivered via a digital health self-management platform over 6 sessions. This will involve a randomized, multiple baseline, single case experimental design with approximately 3 to 15 adults with cardiac disease. The independent variable for each participant will be the pre-post intervention phase. The dependent variables will be a daily self-report measure of psychological flexibility as well as objective measures of condition self-management (eg, blood pressure readings) and engagement with the app (eg, completing guided mindfulness). One-to-one qualitative interviews will also be conducted to further examine participants' experiences with using the intervention and what factors contribute to or impede successful outcomes. RESULTS: Participant recruitment and data collection began in October 2021, and it is projected that the study findings will be available for dissemination by spring 2022. CONCLUSIONS: The findings will be discussed in terms of how a digital ACT intervention can best meet the needs of cardiac patients. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/33783.

Quantifying Steps During a Timed Up and Go Test Using a Wearable Sensor System: A Laboratory-Based Validation Study in Healthy Young and Older Volunteers.

Mobility is an important factor in maintaining health and independence in an aging population. Facilitating community-dwelling older adults to independently identify signs of functional decline could help reduce disability and frailty development. Step-count from a body-worn sensor system was compared with a criterion measure in healthy young (n = 10) and healthy older adults (n = 10) during a Timed Up and Go test under different conditions. Spearman's rank correlation coefficient indicated strong agreement between the sensor-obtained step-count and that of the criterion measure in both age groups, in all mobility tests. A body-worn sensor system can provide objective, quantitative measures of step-count over short distances in older adults. Future research will examine if step-count alone can be used to identify functional decline and risk of frailty.Clinical Relevance-This demonstrates the correlation between step-count derived from a wearable sensor and a criterion measure over a short distance in older adults.

Delivering Cardiac Rehabilitation Exercise Virtually Using a Digital Health Platform (ECME-CR): Protocol for a Pilot Trial.

BACKGROUND: Exercise-based cardiac rehabilitation is recognized as a core component of cardiovascular disease management and has been shown to reduce all-cause and cardiovascular mortality and reduce the risk of hospital readmission following a cardiac event. However, despite this, the uptake of and long-term adherence to cardiac rehabilitation exercise is poor. Delivering cardiac rehabilitation exercise virtually (ie, allowing patients to participate from their own homes) may be an alternative approach that could enhance uptake and increase adherence. OBJECTIVE: The aim of this study is to assess the feasibility of delivering a virtual cardiac rehabilitation exercise program supported by the Eastern Corridor Medical Engineering - Cardiac Rehabilitation (ECME-CR) platform. METHODS: A convenience sample (n=20) of participants eligible to participate in community-based cardiac rehabilitation exercise will be recruited. Participants will be randomized to one of two study groups. Both study groups will perform the same exercise program, consisting of twice-weekly sessions of 60 minutes each, over an 8-week intervention period. Participants in the intervention group will partake in virtually delivered cardiac rehabilitation exercise classes in their own home. The virtual exercise classes will be delivered to participants using a videoconferencing platform. Participants in the control group will attend the research center for their cardiac rehabilitation exercise classes. Intervention group participants will receive the ECME-CR digital health platform for monitoring during the class and during the intervention period. Outcomes will be assessed at baseline and following the 8-week intervention period. The primary outcome will be exercise capacity as assessed using the 6-minute walk test. Other outcome measures will include heart rate, blood pressure, weight, percentage body fat, muscle strength, and self-reported quality of life. Semistructured interviews will also be conducted with a subset of participants to explore their experiences of using the digital platform. RESULTS: Participant recruitment and data collection will begin in July 2021, and it is anticipated that the study results will be available for dissemination in spring 2022. CONCLUSIONS: This pilot trial will inform the design of a randomized controlled trial that will assess the clinical effectiveness of the ECME-CR digital health platform. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31855.

Accuracy of Wrist-Worn Photoplethysmography Devices at Measuring Heart Rate in the Laboratory and During Free-Living Activities.

This study compared heart rate (HR) measurements taken from two wrist-worn devices; the Empatica E4 and the Apple Watch Series 5, to that taken from a Polar H10 chest strap. Ten healthy adult volunteers took part in a laboratory validation study and performed a treadmill exercise protocol. A single-subject validity study was also conducted to evaluate the accuracy of continuous HR measurements obtained during free-living activities. The participant wore both wrist devices, as well as the Polar H10 for 12-hours, as she continued her habitual daily activities. The key findings of the laboratory study were that the Apple Watch was accurate at assessing HR compared to the Polar H10 with Mean Absolute Percentage Error (MAPE) values < 5% during treadmill exercise. The accuracy of the E4 however was generally poor with MAPE values > 15%. Findings from the single-subject validity study indicate that the Apple Watch produces accurate measurements of HR, whereas the E4 device overestimated HR, except for during the more strenuous activities undertaken where HR was underestimated.Clinical Relevance- The Apple Watch has acceptable accuracy in measuring HR during treadmill exercise and during free-living activities in healthy adult volunteers.

Measurement of Heart Rate Using the Polar OH1 and Fitbit Charge 3 Wearable Devices in Healthy Adults During Light, Moderate, Vigorous, and Sprint-Based Exercise: Validation Study.

BACKGROUND: Accurate, continuous heart rate measurements are important for health assessment, physical activity, and sporting performance, and the integration of heart rate measurements into wearable devices has extended its accessibility. Although the use of photoplethysmography technology is not new, the available data relating to the validity of measurement are limited, and the range of activities being performed is often restricted to one exercise domain and/or limited intensities. OBJECTIVE: The primary objective of this study was to assess the validity of the Polar OH1 and Fitbit Charge 3 devices for measuring heart rate during rest, light, moderate, vigorous, and sprint-type exercise. METHODS: A total of 20 healthy adults (9 female; height: mean 1.73 [SD 0.1] m; body mass: mean 71.6 [SD 11.0] kg; and age: mean 40 [SD 10] years) volunteered and provided written informed consent to participate in the study consisting of 2 trials. Trial 1 was split into 3 components: 15-minute sedentary activities, 10-minute cycling on a bicycle ergometer, and incremental exercise test to exhaustion on a motorized treadmill (18-42 minutes). Trial 2 was split into 2 components: 4 × 15-second maximal sprints on a cycle ergometer and 4 × 30- to 50-m sprints on a nonmotorized resistance treadmill. Data from the 3 devices were time-aligned, and the validity of Polar OH1 and Fitbit Charge 3 was assessed against Polar H10 (criterion device). Validity was evaluated using the Bland and Altman analysis, Pearson moment correlation coefficient, and mean absolute percentage error. RESULTS: Overall, there was a very good correlation between the Polar OH1 and Polar H10 devices (r=0.95), with a mean bias of -1 beats·min-1 and limits of agreement of -20 to 19 beats·min-1. The Fitbit Charge 3 device underestimated heart rate by 7 beats·min-1 compared with Polar H10, with a limit of agreement of -46 to 33 beats·min-1 and poor correlation (r=0.8). The mean absolute percentage error for both devices was deemed acceptable (<5%). Polar OH1 performed well across each phase of trial 1; however, validity was worse for trial 2 activities. Fitbit Charge 3 performed well only during rest and nonsprint-based treadmill activities. CONCLUSIONS: Compared with our criterion device, Polar OH1 was accurate at assessing heart rate, but the accuracy of Fitbit Charge 3 was generally poor. Polar OH1 performed worse during trial 2 compared with the activities in trial 1, and the validity of the Fitbit Charge 3 device was particularly poor during our cycling exercises.

Enablers and obstacles to implementing remote monitoring technology in cardiac care: A report from an interactive workshop.

An ageing population and chronic disease are putting pressure on the Irish health system. The field of eHealth is rapidly evolving and has the potential to become an important component of healthcare, but there appears to be a gap currently between research in this field and the integration of eHealth technology into clinical practice. During the eHealth Ireland Ecosystem Conference held in April 2018, a workshop was conducted to explore the barriers and facilitators to the adoption of eHealth technology, particularly remote monitoring systems in community and home cardiac care. Participants included clinicians, academic researchers, technologists, patient advocates, policy makers, and representatives from the health service. The conversations in the workshop pivoted around why technology systems in cardiac care rarely moved beyond the research project stage and what can be done to address this issue. The discussions in the workshop focused around the lack of funding available, the need for reimbursement models, the lack of awareness about remote monitoring, the angst about who is responsible for the data generated, the design of systems, regulatory standards, and the increasing demand on services, education, and patient empowerment.

Data Mining and Fusion of Unobtrusive Sensing Solutions for Indoor Activity Recognition.

This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen: (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of: (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance-This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.

The Impact of a Cycled Lighting Intervention on Nursing Home Residents: A Pilot Study.

PURPOSE: Achieving adequate levels of illumination to stimulate the circadian system can be difficult in a nursing home. The aim of this study was to examine the impact that a 4-week cycled lighting intervention had on activity, sleep, and mood in older adults living in a nursing home. PATIENTS AND METHODS: Ten residents were given an activity monitor to objectively measure activity and sleep, and subjective mood scores were also recorded during the study period. The cycled lighting intervention was designed to mimic normal natural daylight. RESULTS: Some participants responded positively to the lighting intervention showing improvements in activity levels, sleep, and mood, while others showed no change or a continued decline. CONCLUSION: Although the results are inconclusive, a cycled lighting intervention remains a potentially promising intervention in the nursing home setting. Further studies with more robust measurements and a larger, more homogeneous cohort are required to investigate this further.

Physical Activity Monitoring in Patients with Neurological Disorders: A Review of Novel Body-Worn Devices.

AIM: The aim was to conduct a systematic review to examine the literature reporting the validity and reliability of wearable physical activity monitoring in individuals with neurological disorders. METHOD: A systematic search of the literature was performed using a specific search strategy in PubMed and CINAHL. A search constraint of articles published in English, including human participants, published between January 2008 and March 2017 was applied. Peer-reviewed studies which enrolled adult participants with any neurological disorder were included. For the studies which sought to explore the validity of activity monitors, the outcomes measured using the monitor were compared to a criterion measure of physical activity. The studies' methodological quality was assessed using an adapted version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) framework. Data extracted from each study included the following: characteristics of the study participants, study setting, devices used, study protocol/methods, outcomes measured, and the validity/reliability of measurement produced. RESULTS: Twenty-three studies examining the validity and reliability of 16 different monitors were included. The identified studies comprised participants with a range of different disorders of neurological origin. The available evidence suggests that biaxial or triaxial accelerometer devices positioned around the ankle produce the most accurate step count measurements in patients with neurological disorders. The findings regarding the reliability and validity of activity counts and energy expenditure are largely inconclusive in this population. DISCUSSION: Ankle-worn biaxial or triaxial accelerometer-type devices provide the most accurate measurement of physical activity. However, further work is required in this field before wearable activity monitoring can be more widely implemented clinically. Standardised activity monitoring protocols are required for implementing these devices in clinical trials and clinical practice, and consensus is required as to the reporting and interpretation of derived variables.

The limb movement analysis of rehabilitation exercises using wearable inertial sensors.

Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis.

Leveraging IMU data for accurate exercise performance classification and musculoskeletal injury risk screening.

Inertial measurement units (IMUs) are becoming increasingly prevalent as a method for low cost and portable biomechanical analysis. However, to date they have not been accepted into routine clinical practice. This is often due to a disconnect between translating the data collected by the sensors into meaningful and actionable information for end users. This paper outlines the work completed by our group in attempting to achieve this. We discuss the conceptual framework involved in our work, the methodological approach taken in analysing sensor signals and discuss possible application models. Our work indicates that IMU based systems have the potential to bridge the gap between laboratory and clinical movement analysis. Future studies will focus on collecting a diverse range of movement data and using more sophisticated data analysis techniques to refine systems.