Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders

Feasibility of using low-cost markerless motion capture for assessing functional outcomes after lower extremity musculoskeletal cancer surgery

BACKGROUND

Physical limitations are frequent and debilitating after sarcoma treatment. Markerless motion capture (MMC) could measure these limitations. Historically expensive cumbersome systems have posed barriers to clinical translation.

RESEARCH QUESTION

Can inexpensive MMC [using Microsoft KinectTM] assess functional outcomes after sarcoma surgery, discriminate between tumour sub-groups and agree with existing assessments?

METHODS

Walking, unilateral stance and kneeling were measured in a cross-sectional study of patients with lower extremity sarcomas using MMC and standard video. Summary measures of temporal, balance, gait and movement velocity were derived. Feasibility and early indicators of validity of MMC were explored by comparing MMC measures i) between tumour sub-groups; ii) against video and iii) with established sarcoma tools [Toronto Extremity Salvage Score (TESS)), Musculoskeletal Tumour Rating System (MSTS), Quality of life-cancer survivors (QoL-CS)]. Statistical analysis was conducted using SPSS v19. Tumour sub-groups were compared using Mann-Whitney U tests, MMC was compared to existing sarcoma measures using correlations and with video using Intraclass correlation coefficient agreement.

RESULTS

Thirty-four adults of mean age 43 (minimum value-maximum value 19-89) years with musculoskeletal tumours in the femur (19), pelvis/hip (3), tibia (9), or ankle/foot (3) participated; 27 had limb sparing surgery and 7 amputation. MMC was well-tolerated and feasible to deliver. MMC discriminated between surgery groups for balance (p<0.05*), agreed with video for kneeling times [ICC = 0.742; p = 0.001*] and showed moderate relationships between MSTS and gait (p = 0.022*, r = -0.416); TESS and temporal outcomes (p = 0.016* and r = -0.0557*), movement velocity (p = 0.021*, r = -0.541); QoL-CS and balance (p = 0.027*, r = 0.441) [* = statistical significance]. As MMC uncovered important relationships between outcomes, it gave an insight into how functional impairments, balance, gait, disabilities and quality of life (QoL) are associated with each other. This gives an insight into mechanisms of poor outcomes, producing clinically useful data i.e. data which can inform clinical practice and guide the delivery of targeted rehabilitation. For example, patients presenting with poor balance in various activities can be prescribed with balance rehabilitation and those with difficulty in movements or activity transitions can be managed with exercises and training to improve the quality and efficiency of the movement.

SIGNIFICANCE

In this first study world-wide, investigating the use of MMC after sarcoma surgery, MMC was found to be acceptable and feasible to assess functional outcomes in this cancer population. MMC demonstrated early indicators of validity and also provided new knowledge that functional impairments are related to balance during unilateral stance and kneeling, gait and movement velocity during kneeling and these outcomes in turn are related to disabilities and QoL. This highlighted important relationships between different functional outcomes and QoL, providing valuable information for delivering personalised rehabilitation. After completing future validation work in a larger study, this approach can offer promise in clinical settings. Low-cost MMC shows promise in assessing patient's impairments in the hospitals or their homes and guiding clinical management and targeted rehabilitation based on novel MMC outcomes affected, therefore providing an opportunity for delivering personalised exercise programmes and physiotherapy care delivery for this rare cancer.

An Algorithm for Activity Recognition and Assessment of Adults Poststroke

IMPORTANCE

Stroke is the leading cause of long-term disability in the United States. Providers have no robust tools to objectively and accurately measure the activity of people with stroke living at home.

OBJECTIVE

To explore the integration of validated upper extremity assessments poststroke within an activity recognition system.

DESIGN

Exploratory descriptive study using data previously collected over 3 mo to report on algorithm testing and assessment integration.

SETTING

Data were collected in the homes of community-dwelling participants.

PARTICIPANTS

Participants were at least 6 mo poststroke, were able to ambulate with or without an assistive device, and self-reported some difficulty using their arm in everyday activities.

OUTCOMES AND MEASURES

The activity detection algorithm's accuracy was determined by comparing its activity labels with manual labels. The algorithm integrated assessment by describing the quality of upper extremity movement, which was determined by reporting extent of reach, mean and maximum speed during movement, and smoothness of movement.

RESULTS

Sixteen participants (9 women, 7 men) took part in this study, with an average age of 63.38 yr (SD = 12.84). The algorithm was highly accurate in correctly identifying activities, with 87% to 95% accuracy depending on the movement. The algorithm was also able to detect the quality of movement for upper extremity movements.

CONCLUSIONS AND RELEVANCE

The algorithm was able to accurately identify in-kitchen activities performed by adults poststroke. Information about the quality of these movements was also successfully calculated. This algorithm has the potential to supplement clinical assessments in treatment planning and outcomes reporting. Plain-Language Summary: This study shows that clinical algorithms have the potential to inform occupational therapy practice by providing clinically relevant data about the in-home activities of adults poststroke. The algorithm accurately identified activities that were performed in the kitchen by adults poststroke. The algorithm also identified the quality of upper extremity movements of people poststroke who were living at home.

A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups

Falls and frailty status are often associated with a decline in physical capacity and multifactorial assessment is highly recommended. Based on the functional and biomechanical parameters measured during clinical tests with an accelerometer integrated into smart eyeglasses, the purpose was to characterize a population of older adults through an unsupervised analysis into different physical performance groups. A total of 84 participants (25 men and 59 women) over the age of sixty-five (age: 74.17 ± 5.80 years; height: 165.70 ± 8.22 cm; body mass: 68.93 ± 13.55 kg) performed a 30 s Sit-to-Stand test, a six-minute walking test (6MWT), and a 3 m Timed Up and Go (TUG) test. The acceleration data measured from the eyeglasses were processed to obtain six parameters: the number of Sit-to-Stands, the maximal vertical acceleration values during Sit-to-Stand movements, step duration and length, and the duration of the TUG test. The total walking distance covered during the 6MWT was also retained. After supervised analyses comparison (i.e., ANOVAs), only one of the parameters (i.e., step length) differed between faller groups and no parameters differed between frail and pre-frail participants. In contrast, unsupervised analysis (i.e., clustering algorithm based on K-means) categorized the population into three distinct physical performance groups (i.e., low, intermediate, and high). All the measured parameters discriminated the low- and high-performance groups. Four of the measured parameters differentiated the three groups. In addition, the low-performance group had a higher proportion of frail participants. These results are promising for monitoring activities in older adults to prevent the decline of physical capacities.

Spacetimematter of aging - The material temporalities of later life

Material gerontology poses the question of how aging processes are co-constituted in relation to different forms of (human and non-human) materiality. This paper makes a novel contribution by asking when aging processes are co-constituted and how these temporalities of aging are entangled with different forms of materiality. In this paper, we explore the entanglements of temporality and materiality in shaping later life by framing them as spacetimematters (Barad, 2013). By drawing on empirical examples from data from a qualitative case study in a long-term care (LTC) facility, we ask how the entanglement of materiality and temporality of a fall-detection sensor co-constitutes aging. We focus on two types of material temporality that came to matter in age-boundary-making practices at this site: the material temporality of a technology-in-training and the material temporality of (false) alarms. Both are interwoven, produced and reproduced through spacetimematterings that established age-boundaries. Against the backdrop of these findings, we propose to understand age(ing) as a situated, distributed, more-than-human process of practices: It emerges in an assemblage of technological innovation discourses, problematizations of demographic change, digitized and analog practices of care and caring, bodily functioning, daily routines, institutionalized spaces and much more. Finally, we discuss the role power plays in those spacetimematterings of aging and conclude with a research outlook for material gerontology.

Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation

Those who survive the initial incidence of a stroke experience impacts on daily function. As a part of the rehabilitation process, it is essential for clinicians to monitor patients' health status and recovery progress accurately and consistently; however, little is known about how patients function in their own homes. Therefore, the goal of this study was to develop, train, and test an algorithm within an ambient, in-home depth sensor system that can classify and quantify home activities of individuals post-stroke. We developed the Daily Activity Recognition and Assessment System (DARAS). A daily action logger was implemented with a Foresite Healthcare depth sensor. Daily activity data were collected from seventeen post-stroke participants' homes over three months. Given the extensive amount of data, only a portion of the participants' data was used for this specific analysis. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network, which learns rich spatial-temporal features from both depth and skeletal joint data, fuses the prediction outputs from a customized 3D convolutional-de-convolutional network, customized region convolutional 3D network, and a proposed region hierarchical co-occurrence network. The per-frame precision and per-action precision were 0.819 and 0.838, respectively, on the test set. The outcomes from the DARAS can help clinicians to provide more personalized rehabilitation plans that benefit patients.

A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults

Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.

Free-living monitoring of ambulatory activity after treatments for lower extremity musculoskeletal cancers using an accelerometer-based wearable - a new paradigm to outcome assessment in musculoskeletal oncology?

PURPOSE

Ambulatory activity (walking) is affected after sarcoma surgery yet is not routinely assessed. Small inexpensive accelerometers could bridge the gap. Study objectives investigated, whether in patients with lower extremity musculoskeletal tumours: (A) it was feasible to conduct ambulatory activity assessments in patient's homes using an accelerometer-based wearable (AX3, Axivity). (B) AX3 assessments produced clinically useful data, distinguished tumour sub-groups and related to existing measures.

METHODS

In a prospective cross-sectional pilot, 34 patients with musculoskeletal tumours in the femur/thigh (19), pelvis/hip (3), tibia/leg (9), or ankle/foot (3) participated. Twenty-seven had limb-sparing surgery and seven amputation. Patients were assessed using a thigh-worn monitor. Summary measures of volume (total steps/day, total ambulatory bouts/day, mean bout length), pattern (alpha), and variability (S₂) of ambulatory activity were derived.

RESULTS

AX3 was well-tolerated and feasible to use. Outcomes compared to literature but did not distinguish tumour sub-groups. Alpha negatively correlated with disability (walking outside (r=-418, p = 0.042*), social life (r=-0.512, p = 0.010*)). Disability negatively predicted alpha (unstandardised co-efficient= -0.001, R²=0.186, p = 0.039*).

CONCLUSIONS

A wearable can assess novel attributes of walking; volume, pattern, and variability after sarcoma surgery. Such outcomes provide valuable information about people's physical performance in their homes, which can guide rehabilitation. Implications for rehabilitationRoutine capture of ambulatory activity by sarcoma services in peoples' homes can provide important information about individuals "actual" physical activity levels and limitations after sarcoma surgery to inform personalised rehabilitation and care needs, including timely referral for support.Routine remote ambulatory monitoring about out of hospital activity can support personalised care for patients, including identifying high risk patients who need rapid intervention and care closer to home.Use of routine remote ambulatory monitoring could enhance delivery of evidence-based care closer to peoples' homes without disrupting their daily routine and therefore reducing patient and carer burden.Collection of data close to home using questionnaires and objective community assessment could be more cost effective and comprehensive than in-hospital assessment and could reduce the need for hospital attendance, which is of importance to vulnerable patients, particularly during the Covid-19 pandemic.

Longitudinal analysis of aging in place at TigerPlace: Resident function and well-being

This paper reports on a longitudinal eight-year analysis (2011-2019) of trajectory of function and well-being residents of TigerPlace Aging in Place (AIP) model of care. Residents were routinely assessed using standard health assessment instruments. Average scores from each measure were examined for changes or trends in resident function; decline over time was calculated. Scores for depression, mental health subscale Short Form Health Survey-12 (SF-12) remained stable over time. Mini Mental State Exam declined to mild dementia range (21-24). Physical measures SF-12 physical health subscale, ADLs, and IADLs declined slightly, while fall risk increased over time. When yearly trends in AIP were modeled with a referent group there was no significant worsening of functioning. The length of stay for TigerPlace residents continued to remain stable at nearly 30 months. Residents maintained function in the environment of their choice longer at cost less than nursing homes, and just above residential care cost.

State-of-the-Art Wearable Sensors and Possibilities for Radar in Fall Prevention

Radar technology is constantly evolving, and new applications are arising, particularly for the millimeter wave bands. A novel application for radar is gait monitoring for fall prevention, which may play a key role in maintaining the quality of life of people as they age. Alarming statistics indicate that one in three adults aged 65 years or older will experience a fall every year. A review of the sensors used for gait analysis and their applications to technology-based fall prevention interventions was conducted, focusing on wearable devices and radar technology. Knowledge gaps were identified, such as wearable radar development, application specific signal processing and the use of machine learning algorithms for classification and risk assessment. Fall prevention through gait monitoring in the natural environment presents significant opportunities for further research. Wearable radar could be useful for measuring gait parameters and performing fall risk-assessment using statistical methods, and could also be used to monitor obstacles in real-time.

Factors influencing community-dwelling older adults' readiness to adopt smart home technology: A qualitative exploratory study

AIMS

Ageing-in-place for older people could be more feasible with the support of smart home technology. Ageing in-place may maximize the independence of older adults and enhance their well-being and quality of life, while decreasing the financial burden of residential care costs, and addressing workforce shortages. However, the uptake of smart home technology is very low among older adults. Accordingly, the aim of this study was to explore factors influencing community-dwelling older adults' readiness to adopt smart home technology.

DESIGN

A qualitative exploratory study design was utilized.

METHODS

Descriptive data were collected between 2019 and 2020 to provide context of sample characteristics for community-dwelling older adults aged ≥65 years. Qualitative data were collected via semi-structured interviews and focus groups, to generate an understanding of older adult's perspectives. Thematic analysis of interviews and focus group transcripts was completed. The Elderadopt model was the conceptual framework used in the analysis of the findings.

RESULTS

Several factors influenced community-dwelling older adults' (N = 19) readiness to adopt smart home technology. Five qualitative themes were identified: knowledge, health and safety, independence, security and cost.

CONCLUSION

Community-dwelling older adults were open to adopting smart home technology to support independence despite some concerns about security and loss of privacy. Opportunities to share information about smart home technology need to be increased to promote awareness and discussion.

IMPACT

Wider adoption of smart home technology globally into the model of aged care can have positive impacts on caregiver burden, clinical workforce, health care utilization and health care economics. Nurses, as the main providers of healthcare in this sector need to be knowledgeable about the options available and be able to provide information and respond to questions know about ageing-in-place technologies to best support older adults and their families.

The accuracy and predictability of micro Doppler radar signature projection algorithm measuring functional movement in NCAA athletes

BACKGROUND

Development of accessible cost-effective technology to objectively, reliably, and accurately predict musculoskeletal injury risk could aid the effort to prevent chronic pain and disability. Recent work on micro-Doppler radar suggests it merits investigation towards these goals. The micro-Doppler signals that are created can infer differences in gross movements such as walking versus crawling in military settings where direct vision is not possible. Unique micro-Doppler signals may be able to identify more subtle movement patterns which would not be easily seen by the human eye.

RESEARCH QUESTION

Can micro Doppler radar predictably and accurately identify subtle differences in movement conditions?

METHODS

This is a cross sectional study recruiting NCAA athletes to jump in front of the micro-Doppler radar barefoot, with shoes, and shoes with a heel lift. The micro-Doppler radar signature projection algorithm was developed to determine whether the radar is able to distinguish the three distinct movement patterns.

RESULTS

Confusion matrices were used to visualize the performance of the support-vector machine at the 80/20 test/train split correctly classifying barefoot subjects, shoes and heel lift, and shoes correctly at 0° with respect to the radar 90.9 %, 86.7 %, and 89.5 % of the time, respectively. At 90° with respect to the radar, it was successful 94.1 %, 100 %, and 80 % of the time, respectively.

CONCLUSION

This study suggests that the micro-Doppler radar signature projection algorithm is highly accurate and able to predict subtle differences in movement that are not readily observed with conventional motion capture systems. Future studies are needed to better understand if micro-Doppler signals can identify pathologic movement patterns or movement that is associated with increased risk of injury.

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis

BACKGROUND

Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients' natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain.

OBJECTIVE

This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain.

METHODS

A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem.

RESULTS

We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42.

CONCLUSIONS

Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians' real-world knowledge when developing pain-assessing machine learning models improves the model's performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.

Illuminating the dark spaces of healthcare with ambient intelligence

Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.

Care Transition Decisions After a Fall-related Emergency Department Visit: A Qualitative Study of Patients' and Caregivers' Experiences

OBJECTIVE

Falls are a leading cause of injury-related emergency department (ED) visits and may serve as a sentinel event for older adults, leading to physical and psychological injury. Our primary objective was to characterize patient- and caregiver-specific perspectives about care transitions after a fall.

METHODS

Using a semistructured interview guide, we conducted in-depth, qualitative interviews using grounded theory methodology. We included patients enrolled in the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare) trial aged 65 years and older who had an ED visit for a fall and their caregivers. Patients with cognitive impairment (CI) were interviewed in patient-caregiver dyads. Domains assessed included the postfall recovery period, the skilled nursing facility (SNF) placement decision-making process, and the ease of obtaining outpatient follow-up. Interviews were audio-recorded, transcribed verbatim, and coded and analyzed for a priori and emergent themes.

RESULTS

A total of 22 interviews were completed with 10 patients, eight caregivers, and four patient-caregiver dyads within the 6-month period after initial ED visits. Patients were on average 83 years old, nine of 14 were female, and two of 14 had CI. Six of 12 caregivers were interviewed in reference to a patient with CI. We identified four overarching themes: 1) the fall as a trigger for psychological and physiological change, 2) SNF placement decision-making process, 3) direct effect of fall on caregivers, and 4) barriers to receipt of recommended follow-up.

CONCLUSIONS

Older adults presenting to the ED after a fall report physical limitations and a prominent fear of falling after their injury. Caregivers play a vital role in securing the home environment; the SNF placement decision-making process; and navigating the transition of care between the ED, SNF, and outpatient visits after a fall. Clinicians should anticipate and address feelings of isolation, changes in mobility, and fear of falling in older adults seeking ED care after a fall.

Ecological Gait as a Fall Indicator in Older Adults: A Systematic Review

BACKGROUND AND OBJECTIVES

Falls represent a major threat for elders, affecting their life quality and expectancy. Clinical tests and questionnaires showed low diagnostic value with respect to fall risk. Modern sensor technology allows in-home gait assessments, with the possibility to register older adults' ecological mobility and, potentially, to improve accuracy in determining fall risk. Hence, we studied the correlation between standardized assessments and ecological gait measures, comparing their ability to identify fall risk and predict prospective falls.

RESEARCH DESIGN AND METHOD

A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement guidelines.

RESULTS

From a total of 938 studies screened, nine articles with an observational study design were included. Evidence from selected works was subcategorized in (i) correlations between ecological and clinical measures and comparative statistics of (ii) prospective fall prediction and (iii) fall risk identification. A large number of correlations were observed between single ecological gait assessments and multiple clinical fall risk evaluations. Moreover, the combination of daily-life features and clinical tests outcomes seemed to improve diagnostic accuracy in fall risk identification and fall prediction. However, it was not possible to understand the extent of this enhancement due to the high variability in models' parameters.

DISCUSSION AND IMPLICATIONS

Evidence suggested that sensor-based ecological assessments of gait could boost diagnostic accuracy of fall risk measurement protocols if used in combination with clinical tests. Nevertheless, further studies are needed to understand what ecological features of gait should be considered and to standardize models' definition.

Innovative Assisted Living Tools, Remote Monitoring Technologies, Artificial Intelligence-Driven Solutions, and Robotic Systems for Aging Societies: Systematic Review

BACKGROUND

The increase in life expectancy and recent advancements in technology and medical science have changed the way we deliver health services to the aging societies. Evidence suggests that home telemonitoring can significantly decrease the number of readmissions, and continuous monitoring of older adults' daily activities and health-related issues might prevent medical emergencies.

OBJECTIVE

The primary objective of this review was to identify advances in assistive technology devices for seniors and aging-in-place technology and to determine the level of evidence for research on remote patient monitoring, smart homes, telecare, and artificially intelligent monitoring systems.

METHODS

A literature review was conducted using Cumulative Index to Nursing and Allied Health Literature Plus, MEDLINE, EMBASE, Institute of Electrical and Electronics Engineers Xplore, ProQuest Central, Scopus, and Science Direct. Publications related to older people's care, independent living, and novel assistive technologies were included in the study.

RESULTS

A total of 91 publications met the inclusion criteria. In total, four themes emerged from the data: technology acceptance and readiness, novel patient monitoring and smart home technologies, intelligent algorithm and software engineering, and robotics technologies. The results revealed that most studies had poor reference standards without an explicit critical appraisal.

CONCLUSIONS

The use of ubiquitous in-home monitoring and smart technologies for aged people's care will increase their independence and the health care services available to them as well as improve frail elderly people's health care outcomes. This review identified four different themes that require different conceptual approaches to solution development. Although the engineering teams were focused on prototype and algorithm development, the medical science teams were concentrated on outcome research. We also identified the need to develop custom technology solutions for different aging societies. The convergence of medicine and informatics could lead to the development of new interdisciplinary research models and new assistive products for the care of older adults.

Application of Wearable Inertial Sensors and A New Test Battery for Distinguishing Retrospective Fallers from Non-fallers among Community-dwelling Older People

Considering the challenge of population ageing and the substantial health problem among the elderly population from falls, the purpose of this study was to verify whether it is possible to distinguish accurately between older fallers and non-fallers, based on data from wearable inertial sensors collected during a specially designed test battery. A comprehensive but practical test battery using 5 wearable inertial sensors for multifactorial fall risk assessment was designed. This was followed by an experimental study on 196 community-dwelling Korean older women, categorized as fallers (N1 = 82) and non-fallers (N2 = 114) based on prior history of falls. Six machine learning models (logistic regression, naïve bayes, decision tree, random forest, boosted tree and support vector machine) were proposed for faller classification. Results indicated that compared with non-fallers, fallers performed significantly worse on the test battery. In addition, the application of sensor data and support vector machine for faller classification achieved an overall accuracy of 89.4% with 92.7% sensitivity and 84.9% specificity. These findings suggest that wearable inertial sensor based systems show promise for elderly fall risk assessment, which could be implemented in clinical practice to identify "at-risk" individuals reliably to promote proactive fall prevention.

Falls management framework for supporting an independent lifestyle for older adults: a systematic review

Falls are one of the common health and well-being issues among the older adults. Internet of things (IoT)-based health monitoring systems have been developed over the past two decades for improving healthcare services for older adults to support an independent lifestyle. This research systematically reviews technological applications related to falls detection and falls management. The systematic review was conducted in accordance to the preferred reporting items for systematic reviews and meta-analysis statement (PRISMA). Twenty-four studies out of 806 articles published between 2015 and 2017 were identified and included in this review. Selected studies were related to pre-fall and post-fall applications using motion sensors (10; 41.67%), environment sensors (10; 41.67%) and few studies used the combination of these types of sensors (4; 16.67%). As an outcome of this review, we postulated a falls management framework (FMF). FMF considered pre- and post-fall strategies to support older adults live independently. A part of this approach involved active analysis of sensor data with the aim of helping the older adults manage their risk of fall and stay safe in their home. FMF aimed to serve the researchers, developers, clinicians and policy makers with pre- and post-falls management strategies to enhance the older adults' independent living and well-being.

Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor

This paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequence of body impact, level of impact, and duration of motionlessness. Module II is a timely autonomic notification to relevant persons with context-dependent fall severity alert via electronic communication channels (e.g., smartphone, tablet, or smart TV set). Lastly, Module III is the diagnostic support for caregivers and doctors to have information for making a well-informed decision of first aid or postcure with the chronologically traceable intelligence of information and knowledge found in Modules I and II. The system shall be beneficial to caregivers or doctors, in giving first aid/diagnosis/treatment to the subject, especially, in cases where the subject has lost consciousness and is unable to respond.

Balance and Gait Impairment: Sensor-Based Assessment for Patients With Peripheral Neuropathy

BACKGROUND

Individuals with peripheral neuropathy (PN) frequently experience balance and gait impairments that can lead to poor physical function, falls, and injury. Nurses are aware that patients with cancer experience balance and gait impairments but are unsure of optimal assessment and management strategies.

OBJECTIVES

This article reviews options for balance and gait assessment for patients diagnosed with cancer experiencing PN, describes advantages and limitations of the various options, and highlights innovative, clinically feasible technologies to improve clinical assessment and management.

METHODS

The literature was reviewed to identify and assess the gold standard quantitative measures for assessing balance and gait.

FINDINGS

Gold standard quantitative measures are burdensome for patients and not often used in clinical practice. Sensor-based technologies improve balance and gait assessment options by calculating precise impairment measures during performance of simple clinical tests at the point of care.

Early Detection of Mild Cognitive Impairment With In-Home Monitoring Sensor Technologies Using Functional Measures: A Systematic Review

The aging of the world population is accompanied by a substantial increase in neurodegenerative disorders, such as dementia. Early detection of mild cognitive impairment (MCI), a clinical diagnostic that comes with an increased chance to develop dementias, could be an essential condition for promoting quality of life and independent living, as it would provide a critical window for the implementation of early pharmacological and nonpharmacological interventions. This systematic review aims to investigate the current state of knowledge on the effectiveness of smart home sensors technologies for the early detection of MCI through the monitoring of everyday life activities. This approach offers many advantages, including the continuous measurement of functional abilities in ecological environments. A systematic search of publications in MEDLINE, EMBASE, and CINAHL, before November 2017, was conducted. Seventeen studies were included in this review. Thirteen studies were based on real-life monitoring, with several sensors installed in participants' actual homes, and four studies included scenario-based assessments, in which participants had to complete various tasks in a research lab apartment. In real-life monitoring, the most used indicators of MCI were walking speed and activity/motion in the house. In scenario-based assessment, time of completion, quality of activity completion, number of errors, amount of assistance needed, and task-irrelevant behaviors during the performance of everyday activities predicted MCI in participants. Despite technological limitations and the novelty of the field, smart home technologies represent a promising potential for the early screening of MCI and could support clinicians in geriatric care.

Novel sensing technology in fall risk assessment in older adults: a systematic review

Background

Falls are a major health problem for older adults with significant physical and psychological consequences. A first step of successful fall prevention is to identify those at risk of falling. Recent advancement in sensing technology offers the possibility of objective, low-cost and easy-to-implement fall risk assessment. The objective of this systematic review is to assess the current state of sensing technology on providing objective fall risk assessment in older adults.

Methods

A systematic review was conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA).

Results

Twenty-two studies out of 855 articles were systematically identified and included in this review. Pertinent methodological features (sensing technique, assessment activities, outcome variables, and fall discrimination/prediction models) were extracted from each article. Four major sensing technologies (inertial sensors, video/depth camera, pressure sensing platform and laser sensing) were reported to provide accurate fall risk diagnostic in older adults. Steady state walking, static/dynamic balance, and functional mobility were used as the assessment activity. A diverse range of diagnostic accuracy across studies (47.9% - 100%) were reported, due to variation in measured kinematic/kinetic parameters and modelling techniques.

Conclusions

A wide range of sensor technologies have been utilized in fall risk assessment in older adults. Overall, these devices have the potential to provide an accurate, inexpensive, and easy-to-implement fall risk assessment. However, the variation in measured parameters, assessment tools, sensor sites, movement tasks, and modelling techniques, precludes a firm conclusion on their ability to predict future falls. Future work is needed to determine a clinical meaningful and easy to interpret fall risk diagnosis utilizing sensing technology. Additionally, the gap between functional evaluation and user experience to technology should be addressed.

Older Adults' Perceptions of and Preferences for a Fall Risk Assessment System: Exploring Stages of Acceptance Model

Aging in place is a preferred and cost-effective living option for older adults. Research indicates that technology can assist with this goal. Information on consumer preferences will help in technology development to assist older adults to age in place. The study aim was to explore the perceptions and preferences of older adults and their family members about a fall risk assessment system. Using a qualitative approach, this study examined the perceptions, attitudes, and preferences of 13 older adults and five family members about their experience living with the fall risk assessment system during five points in time. Themes emerged in relation to preferences and expectations about the technology and how it fits into daily routines. We were able to capture changes that occurred over time for older adult participants. Results indicated that there was acceptance of the technology as participants adapted to it. Two themes were present across the five points in time-safety and usefulness. Five stages of acceptance emerged from the data from preinstallation to 2 years postinstallation. Identified themes, stages of acceptance, and design and development considerations are discussed.

Automated classification of pathological gait after stroke using ubiquitous sensing technology

This study uses machine learning methods to distinguish between healthy and pathological gait. Examples of multi-dimensional pathological and normal gait sequences were collected from post-stroke and healthy individuals in a real clinical setting and with two Kinect sensors. The trajectories of rotational angle and global velocity of selected body joints (hips, spine, shoulders, neck, knees and ankles) over time formed the gait sequences. The combination of k nearest neighbor (kNN) and dynamic time warping (DTW) was used for classification. Leave one subject out cross validation was implemented to evaluate the performance of the binary classifier in terms of F1-score in the original feature space, and also in a reduced dimensional feature space using PCA. The pair of k = 1 in kNN and the warping window size 25% of gait sequences in DTW achieved maximum F1-score. Using PCA, pathological gait sequences were discriminated from healthy sequences with the F1-score = 96%.

Testing non-wearable fall detection methods in the homes of older adults

In this paper, we describe two longitudinal studies in which fall detection sensor technology was tested in the homes of older adults. The first study tested Doppler radar, a two-webcam system, and a depth camera system in ten apartments for two years. This continuous data collection allowed us to investigate the real-world setting of target users and compare the advantages and limitations of each sensor modality. Based on this study, the depth camera was chosen for a current ongoing study in which depth camera systems have been installed in 94 additional older adult apartments. We include a discussion of the different sensor systems, the pros and cons of each, and results of the fall detection and false alarms in the older adult homes.

Randomized Trial of Intelligent Sensor System for Early Illness Alerts in Senior Housing

OBJECTIVES

Measure the clinical effectiveness and cost effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. This sensor system has demonstrated in pilot studies to detect changes in function and in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness.

DESIGN

Prospective intervention study in 13 assisted living (AL) communities of 171 residents randomly assigned to intervention (n=86) or comparison group (n=85) receiving usual care.

METHODS

Intervention participants lived with the sensor system an average of one year.

MEASUREMENTS

Continuous data collected 24 hours/7 days a week from motion sensors to measure overall activity, an under mattress bed sensor to capture respiration, pulse, and restlessness as people sleep, and a gait sensor that continuously measures gait speed, stride length and time, and automatically assess for increasing fall risk as the person walks around the apartment. Continuously running computer algorithms are applied to the sensor data and send health alerts to staff when there are changes in sensor data patterns.

RESULTS

The randomized comparison group functionally declined more rapidly than the intervention group. Walking speed and several measures from GaitRite, velocity, step length left and right, stride length left and right, and the fall risk measure of functional ambulation profile (FAP) all had clinically significant changes. The walking speed increase (worse) and velocity decline (worse) of 0.073 m/s for comparison group exceeded 0.05 m/s, a value considered to be a minimum clinically important difference. No differences were measured in health care costs.

CONCLUSIONS

These findings demonstrate that sensor data with health alerts and fall alerts sent to AL nursing staff can be an effective strategy to detect and intervene in early signs of illness or functional decline.

Assessment of Fall Characteristics From Depth Sensor Videos

Falls are a major source of death and disability in older adults; little data, however, are available about the etiology of falls in community-dwelling older adults. Sensor systems installed in independent and assisted living residences of 105 older adults participating in an ongoing technology study were programmed to record live videos of probable fall events. Sixty-four fall video segments from 19 individuals were viewed and rated using the Falls Video Assessment Questionnaire. Raters identified that 56% (n = 36) of falls were due to an incorrect shift of body weight and 27% (n = 17) from losing support of an external object, such as an unlocked wheelchair or rolling walker. In 60% of falls, mobility aids were in the room or in use at the time of the fall. Use of environmentally embedded sensors provides a mechanism for real-time fall detection and, ultimately, may supply information to clinicians for fall prevention interventions. [Journal of Gerontological Nursing, 43(7), 13-19.].

Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study

Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects including fear of falling, loss of independence, and disability. The goals of the WIISEL project were to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk of older adults in the home setting; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. The WIISEL system was based on a pair of electronic insoles, able to transfer data to a commercially available smartphone, which was used to wirelessly collect data in real time from the insoles and transfer it to a backend computer server via mobile internet connection and then onwards to a gait analysis tool. Risk of falls was calculated by the system using a novel Fall Risk Index (FRI) based on multiple gait parameters and gait pattern recognition. The system was tested by twenty-nine older users and data collected by the insoles were compared with standardized functional tests with a concurrent validity approach. The results showed that the FRI captures the risk of falls with accuracy that is similar to that of conventional performance-based tests of fall risk. These preliminary findings support the idea that theWIISEL system can be a useful research tool and may have clinical utility for long-term monitoring of fall risk at home and in the community setting.

Upper extremity movement reliability and validity of the Kinect version 2

PURPOSE

Studies have shown that marker-less motion detection systems, such as the first generation Kinect (Kinect 1), have good reliability and potential for clinical application. Studies of the second generation Kinect (Kinect 2) have shown a large range of accuracy relative to balance and joint localization; however, few studies have investigated the validity and reliability of the Kinect 2 for upper extremity motion. This investigation compared reliability and validity among the Kinect 1, Kinect 2 and a video motion capture (VMC) system for upper extremity movements.

DESIGN

One healthy, adult male performed six upper extremity movements during two separate sessions. All movements were recorded on the Kinect 1, Kinect 2 and VMC simultaneously. Data were analyzed using MATLAB (Natick, MA), Microsoft Excel (Redmond, WA), and SPSS (Armonk, NY).

RESULTS

Results indicated good reliability for both Kinects within a day; results between days were inconclusive for both devices due to the inability to exactly repeat the desired movements. Range of motion (ROM) magnitudes for both Kinects were different from the VMC, yet patterns of motion were very highly correlated for both devices.

CONCLUSION

Simple transformations of Kinect data could bring magnitudes in line with those of the VMC, allowing the Kinects to be used in a clinical setting. Implications for Rehabilitation The clinical implications of the investigation support the notion that the Kinects could be used in the clinical setting if an understanding of their limitations exists. Using the Kinects to make assessments with a given data collection session is acceptable. Using the Kinects to make comparisons across different days such as before or after an intervention should be approached with caution. The Kinect 2 provides a more cost effective option compared to the VMC. Additionally, the Kinect is more portable, requires less time to set-up, and takes up less space, thus increasing its overall usability compared to the VMC.

Digital technology to enable aging in place

Aging, both of individuals and populations, presents challenges and opportunities. The multitude of morbidities and disabilities that are a too common component of aging represent significant challenges to individuals, their families and to healthcare systems. Aging in place is the ability to safely and comfortably maintain an independent and high quality of life in one's own home and community and is a highly desirable goal of most individuals with the additional benefit of significantly impacting the impending enormous healthcare burden. In order to make this possible, new care models that take advantage of novel technologies for tracking important physiologic and safety parameters need to be developed and implemented. By thoughtfully doing so, it can be possible to seamlessly provide preventative interventions when and as needed, detect the earliest signs of aggravation of chronic conditions, or identify and respond to any emergency situations, such as falls or cardiac arrest. In contrast to current approaches, caring for elderly individuals in their homes based on a digital technology infrastructure could be effective and cost-saving. In this review, we provide an overview of the characteristics of potential digital solutions applicable to creative aging along with the existing evidence supporting their ability to improve care, increase quality of life, and substantially decrease the emotional and financial costs associated with aging.

Exploring the feasibility and acceptability of sensor monitoring of gait and falls in the homes of persons with multiple sclerosis

Gait parameters variability and falls are problems for persons with MS and have not been adequately captured in the home. Our goal was to explore the feasibility and acceptability of monitoring of gait and falls in the homes of persons with MS over a period of 30 days. To test the feasibility of measuring gait and falls for 30days in the home of persons with MS, spatiotemporal gait parameters stride length, stride time, and gait speed were compared. A 3D infrared depth imaging system has been developed to objectively measure gait and falls in the home environment. Participants also completed a 16-foot GaitRite electronic pathway walk to validate spatiotemporal parameters of gait (gait speed (cm/s), stride length (cm), and gait cycle time(s)) during the timed 25 foot walking test (T25FWT). We also documented barriers to feasibility of installing the in-home sensors for these participants. The results of the study suggest that the Kinect sensor may be used as an alternative device to measure gait for persons with MS, depending on the desired accuracy level. Ultimately, using in-home sensors to analyze gait parameters in real time is feasible and could lead to better analysis of gait in persons with MS.

A New Paradigm of Technology-Enabled ‘Vital Signs’ for Early Detection of Health Change for Older Adults

Environmentally embedded (nonwearable) sensor technology is in continuous use in elder housing to monitor a new set of ‘vital signs' that continuously measure the functional status of older adults, detect potential changes in health or functional status, and alert healthcare providers for early recognition and treatment of those changes. Older adult participants' respiration, pulse, and restlessness are monitored as they sleep. Gait speed, stride length, and stride time are calculated daily, and automatically assess for increasing fall risk. Activity levels are summarized and graphically displayed for easy interpretation. Falls are detected when they occur and alerts are sent immediately to healthcare providers, so time to rescue may be reduced. Automated health alerts are sent to healthcare staff, based on continuously running algorithms applied to the sensor data, days and weeks before typical signs or symptoms are detected by the person, family members, or healthcare providers. Discovering these new functional status ‘vital signs', developing automated methods for interpreting them, and alerting others when changes occur have the potential to transform chronic illness management and facilitate aging in place through the end of life. Key findings of research in progress at the University of Missouri are discussed in this viewpoint article, as well as obstacles to widespread adoption.