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.

Novel clinically-relevant assessment of upper extremity movement using depth sensors.

BACKGROUND: For individuals post-stroke, home-based programs are necessary to deliver additional hours of therapy outside of the limited time in the clinic. Virtual reality (VR)-based approaches show modest outcomes in improving client function when delivered in the home. The movement sensors used in these VR-based approaches, such as the Microsoft Kinect® have been validated against gold standards tools but have not been used as an assessment of upper extremity movement quality in the stroke population. OBJECTIVES: The purpose of this study was to explore the use of a movement sensor paired with a VR-based intervention to assess upper extremity movement for individuals post-stroke. METHODS: Movement data captured with the Microsoft Kinect® from four separate studies were aggregated for analysis (n = 8 individuals post-stroke, n = 30 individuals without disabilities). For all participants, the skeletal data (x, y, z coordinates for 15 tracked joints) for each game play session were processed in MatLab and movement variables (normalized jerk, movement path ratio, average path sway) were calculated using an OPTICS density-based cluster algorithm. RESULTS: Data from the 30 healthy individuals created a normative baseline for the three kinematic variables. Individuals post-stroke were less efficient and had more jerky movements in both upper extremities as compared to healthy individuals. CONCLUSION: It is feasible to use a movement sensor paired with a VR-based intervention to quantify and qualify upper extremity movement for individuals post-stroke. Further research with a larger cohort is necessary to establish clinical sensitivity and specificity.

Stepping Beyond Assessment: Fall Risk Prediction Models Among Older Adults from Cumulative Change in Gait Parameter Estimates.

Falls significantly affect the health of older adults. Injuries sustained through falls have long-term consequences on the ability to live independently and age in place, and are the leading cause of injury death in the United States for seniors. Early fall risk detection provides an important opportunity for prospective intervention by healthcare providers and home caregivers. In-home depth sensor technologies have been developed for real-time fall detection and gait parameter estimation including walking speed, the sixth vital sign, which has been shown to correlate with the risk of falling. This study evaluates the use of supervised classification for estimating fall risk from cumulative changes in gait parameter estimates as captured by 3D depth sensors placed within the homes of older adult participants. Using recall as the primary metric for model success rate due to the severity of fall injuries sustained by false negatives, we demonstrate an enhancement of assessing fall risk with univariate logistic regression using multivariate logistic regression, support vector, and hierarchical tree-based modeling techniques by an improvement of 18.80%, 31.78%, and 33.94%, respectively, in the 14 days preceding a fall event. Random forest and XGBoost models resulted in recall and precision scores of 0.805 compared to the best univariate regression model of Y-Entropy with a recall of 0.639 and precision of 0.527 for the 14-day window leading to a predicted fall event.

Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography.

Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters. Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure. Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement. Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.

Tracking personalized functional health in older adults using geriatric assessments.

BACKGROUND: Higher levels of functional health in older adults leads to higher quality of life and improves the ability to age-in-place. Tracking functional health objectively could help clinicians to make decisions for interventions in case of health deterioration. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking personalized functional health of older adults using a combination of these assessments. METHODS: We used geriatric assessment data collected from 150 older adults to develop and validate a functional health prediction model based on risks associated with falls, hospitalizations, emergency visits, and death. We used mixed effects logistic regression to construct the model. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Construct validators such as fall risks associated with model predictions, and case studies with functional health trajectories were used to validate the model. RESULTS: The model is shown to separate samples with and without adverse health event outcomes with an area under the receiver operating characteristic curve (AUC) of > 0.85. The model could predict emergency visit or hospitalization with an AUC of 0.72 (95% CI 0.65-0.79), fall with an AUC of 0.86 (95% CI 0.83-0.89), fall with hospitalization with an AUC of 0.89 (95% CI 0.85-0.92), and mortality with an AUC of 0.93 (95% CI 0.88-0.97). Multiple comparisons of means using Turkey HSD test show that model prediction means for samples with no adverse health events versus samples with fall, hospitalization, and death were statistically significant (p < 0.001). Case studies for individual residents using predicted functional health trajectories show that changes in model predictions over time correspond to critical health changes in older adults. CONCLUSIONS: The personalized functional health tracking may provide clinicians with a longitudinal view of overall functional health in older adults to help address the early detection of deterioration trends and decide appropriate interventions. It can also help older adults and family members take proactive steps to improve functional health.

Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges.

Children, parents, older adults, and caregivers routinely use sensor technology as a source of health information and health monitoring. The purpose of this paper is to describe three exemplars of research that used a human-centered approach to engage participants in the development, design, and usability of interventions that integrate technology to promote health. The exemplars are based on current research studies that integrate sensor technology into pediatric, adult, and older adult populations living with a chronic health condition. Lessons learned and considerations for future studies are discussed. Nurses have successfully implemented interventions that use technology to improve health and detect, prevent, and manage diseases in children, families, individuals and communities. Nurses are key stakeholders to inform clinically relevant health monitoring that can support timely and personalized intervention and recommendations.

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.

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.

Enhanced registered nurse care coordination with sensor technology: Impact on length of stay and cost in aging in place housing.

BACKGROUND: When planning the Aging in Place Initiative at TigerPlace, it was envisioned that advances in technology research had the potential to enable early intervention in health changes that could assist in proactive management of health for older adults and potentially reduce costs. PURPOSE: The purpose of this study was to compare length of stay (LOS) of residents living with environmentally embedded sensor systems since the development and implementation of automated health alerts at TigerPlace to LOS of those who are not living with sensor systems. Estimate potential savings of living with sensor systems. METHODS: LOS for residents living with and without sensors was measured over a span of 4.8 years since the implementation of sensor-generated health alerts. The group living with sensors (n = 52) had an average LOS of 1,557 days (4.3 years); the comparison group without sensors (n = 81) was 936 days (2.6 years); p = .0006. Groups were comparable based on admission age, gender, number of chronic illnesses, SF12 physical health, SF12 mental health, Geriatric Depression Scale (GDS), activities of daily living, independent activities of daily living, and mini-mental status examination scores. Both groups, all residents living at TigerPlace since the implementation of health alerts, receive registered nurse (RN) care coordination as the standard of care. DISCUSSION: Results indicate that residents living with sensors were able to reside at TigerPlace 1.7 years longer than residents living without sensors, suggesting that proactive use of health alerts facilitates successful aging in place. Health alerts, generated by automated algorithms interpreting environmentally embedded sensor data, may enable care coordinators to assess and intervene on health status changes earlier than is possible in the absence of sensor-generated alerts. Comparison of LOS without sensors TigerPlace (2.6 years) with the national median in residential senior housing (1.8 years) may be attributable to the RN care coordination model at TigerPlace. Cost estimates comparing cost of living at TigerPlace with the sensor technology vs. nursing home reveal potential saving of about $30,000 per person. Potential cost savings to Medicaid funded nursing home (assuming the technology and care coordination were reimbursed) are estimated to be about $87,000 per person. CONCLUSIONS: Early alerts for potential health problems appear to enhance the current RN care coordination care delivery model at TigerPlace, increasing LOS for those living with sensors to nearly twice that of those who did not. Sensor technology with care coordination has cost saving potential for consumers and Medicaid.

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

PURPOSE OF THE STUDY: Falls are a major problem for the elderly people leading to injury, disability, and even death. An unobtrusive, in-home sensor system that continuously monitors older adults for fall risk and detects falls could revolutionize fall prevention and care. DESIGN AND METHODS: A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The system includes pulse-Doppler radar, a Microsoft Kinect, and 2 web cameras. To collect data for comparison with sensor data and for algorithm development, stunt actors performed falls in participants' apartments each month for 2 years and participants completed fall risk assessments (FRAs) using clinically valid, standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants' gait parameters were measured as they walked on a GAITRite mat. These data were used as ground truth, objective data to use in algorithm development and to compare with radar and Kinect generated variables. RESULTS: All FRAs are highly correlated (p < .01) with the Kinect gait velocity and Kinect stride length. Radar velocity is correlated (p < .05) to all the FRAs and highly correlated (p < .01) to most. Real-time alerts of actual falls are being sent to clinicians providing faster responses to urgent situations. IMPLICATIONS: The in-home FRA and detection system has the potential to help older adults remain independent, maintain functional ability, and live at home longer.

Automated Health Alerts Using In-Home Sensor Data for Embedded Health Assessment.

We present an example of unobtrusive, continuous monitoring in the home for the purpose of assessing early health changes. Sensors embedded in the environment capture behavior and activity patterns. Changes in patterns are detected as potential signs of changing health. We first present results of a preliminary study investigating 22 features extracted from in-home sensor data. A 1-D alert algorithm was then implemented to generate health alerts to clinicians in a senior housing facility. Clinicians analyze each alert and provide a rating on the clinical relevance. These ratings are then used as ground truth for training and testing classifiers. Here, we present the methodology for four classification approaches that fuse multisensor data. Results are shown using embedded sensor data and health alert ratings collected on 21 seniors over nine months. The best results show similar performance for two techniques, where one approach uses only domain knowledge and the second uses supervised learning for training. Finally, we propose a health change detection model based on these results and clinical expertise. The system of in-home sensors and algorithms for automated health alerts provides a method for detecting health problems very early so that early treatment is possible. This method of passive in-home sensing alleviates compliance issues.

Average in-home gait speed: investigation of a new metric for mobility and fall risk assessment of elders.

A study was conducted to assess how a new metric, average in-home gait speed (AIGS), measured using a low-cost, continuous, environmentally mounted monitoring system, compares to a set of traditional physical performance instruments used for mobility and fall risk assessment of elderly adults. Sixteen participants were recruited from a local independent living facility. In addition to having their gait monitored continuously in their home for an average of eleven months, the participants completed a monthly clinical assessment consisting of a set of traditional assessment instruments: Habitual Gait Speed, Timed-Up and Go, Short Physical Performance Battery, Berg Balance Scale--short form, and Multidirectional Reach Test. A methodology is developed to assess which of these instruments may work well with the largest subset of older adults, is best suited for detecting changes in an individual over time, and most reliably captures the true mobility level of an individual. Using the ability of an instrument to predict how an individual would score on all the instruments as a metric, AIGS performs best, having better predictive ability than the traditional instruments. AIGS also displays the best agreement between observed and smoothed values, indicating it has the lowest intra-individual test-retest variability of the instruments. AIGS, measured continuously, during normal everyday activity, represents a significant shift in assessment methodology compared to infrequently assessed, traditional physical performance instruments. Continuous, in-home data may provide a more accurate and precise picture of the physical function of older adults, leading to improved mobility and fall risk assessment.

Quantitative gait measurement with pulse-Doppler radar for passive in-home gait assessment.

In this paper, we propose a pulse-Doppler radar system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed and step time using Doppler radar. The gait parameters have been validated with a Vicon motion capture system in the lab with 13 participants and 158 test runs. The study revealed that for an optimal step recognition and walking speed estimation, a dual radar set up with one radar placed at foot level and the other at torso level is necessary. An excellent absolute agreement with intraclass correlation coefficients of 0.97 was found for step time estimation with the foot level radar. For walking speed, although both radars show excellent consistency they all have a system offset compared to the ground truth due to walking direction with respect to the radar beam. The torso level radar has a better performance (9% offset on average) in the speed estimation compared to the foot level radar (13%-18% offset). Quantitative analysis has been performed to compute the angles causing the systematic error. These lab results demonstrate the capability of the system to be used as a daily gait assessment tool in home environments, useful for fall risk assessment and other health care applications. The system is currently being tested in an unstructured home environment.

A framework for harmonizing sensor data to support embedded health assessment.

The use of in-home and mobile sensing is likely to be a key component of future care and has recently been studied by many research groups world-wide. Researchers have shown that embedded sensors can be used for health assessment such as early illness detection and the management of chronic health conditions. However, research collaboration and data sharing have been hampered by disparate sets of sensors and data collection methods. To date, there have been no studies to investigate common measures that can be used across multiple sites with different types of sensors, which would facilitate large scale studies and reuse of existing datasets. In this paper, we propose a framework for harmonizing heterogeneous sensor data through an intermediate layer, the Conceptual Sensor, which maps physical measures to clinical space. Examples are included for sleep quality and ambulatory physical function.

Automated health alerts from Kinect-based in-home gait measurements.

A method for automatically generating alerts to clinicians in response to changes in in-home gait parameters is investigated. Kinect-based gait measurement systems were installed in apartments in a senior living facility. The systems continuously monitored the walking speed, stride time, and stride length of apartment residents. A framework for modeling uncertainty in the residents' gait parameter estimates, which is critical for robust change detection, is developed; along with an algorithm for detecting changes that may be clinically relevant. Three retrospective case studies, of individuals who had their gait monitored for periods ranging from 12 to 29 months, are presented to illustrate use of the alert method. Evidence suggests that clinicians could be alerted to health changes at an early stage, while they are still small and interventions may be most successful. Additional potential uses are also discussed.

Toward a passive low-cost in-home gait assessment system for older adults.

In this paper, we propose a webcam-based system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed, step time, and step length from a 3-D voxel reconstruction, which is built from two calibrated webcam views. The gait parameters are validated with a GAITRite mat and a Vicon motion capture system in the laboratory with 13 participants and 44 tests, and again with GAITRite for 8 older adults in senior housing. Excellent agreement with intraclass correlation coefficients of 0.99 and repeatability coefficients between 0.7% and 6.6% was found for walking speed, step time, and step length given the limitation of frame rate and voxel resolution. The system was further tested with ten seniors in a scripted scenario representing everyday activities in an unstructured environment. The system results demonstrate the capability of being used as a daily gait assessment tool for fall risk assessment and other medical applications. Furthermore, we found that residents displayed different gait patterns during their clinical GAITRite tests compared to the realistic scenario, namely a mean increase of 21% in walking speed, a mean decrease of 12% in step time, and a mean increase of 6% in step length. These findings provide support for continuous gait assessment in the home for capturing habitual gait.

In-home fall risk assessment and detection sensor system.

Falls are a major problem in older adults. A continuous, unobtrusive, environmentally mounted (i.e., embedded into the environment and not worn by the individual), in-home monitoring system that automatically detects when falls have occurred or when the risk of falling is increasing could alert health care providers and family members to intervene to improve physical function or manage illnesses that may precipitate falls. Researchers at the University of Missouri Center for Eldercare and Rehabilitation Technology are testing such sensor systems for fall risk assessment (FRA) and detection in older adults' apartments in a senior living community. Initial results comparing ground truth (validated measures) of FRA data and GAITRite System parameters with data captured from Microsoft(®) Kinect and pulse-Doppler radar are reported.

Passive sensor technology interface to assess elder activity in independent living.

BACKGROUND: The effectiveness of clinical information systems to improve nursing and patient outcomes depends on human factors, including system usability, organizational workflow, and user satisfaction. OBJECTIVE: The aim of this study was to examine to what extent residents, family members, and clinicians find a sensor data interface used to monitor elder activity levels usable and useful in an independent living setting. METHODS: Three independent expert reviewers conducted an initial heuristic evaluation. Subsequently, 20 end users (5 residents, 5 family members, 5 registered nurses, and 5 physicians) participated in the evaluation. During the evaluation, each participant was asked to complete three scenarios taken from three residents. Morae recorder software was used to capture data during the user interactions. RESULTS: The heuristic evaluation resulted in 26 recommendations for interface improvement; these were classified under the headings content, aesthetic appeal, navigation, and architecture, which were derived from heuristic results. Total time for elderly residents to complete scenarios was much greater than for other users. Family members spent more time than clinicians but less time than residents did to complete scenarios. Elder residents and family members had difficulty interpreting clinical data and graphs, experienced information overload, and did not understand terminology. All users found the sensor data interface useful for identifying changing resident activities. DISCUSSION: Older adult users have special needs that should be addressed when designing clinical interfaces for them, especially information as important as health information. Evaluating human factors during user interactions with clinical information systems should be a requirement before implementation.

Quantitative analysis of 180 degree turns for fall risk assessment using video sensors.

In this paper, we present a method for quantitatively and objectively assessing 180 degree turns using low cost video sensors. A three-dimensional voxel reconstruction, which is built using silhouettes captured from two calibrated web camera views, is used to represent the human body. Experiments were conducted in which participants performed the standard Timed Up and Go tests where 180 degree turns are evaluated. Our two calibrated cameras captured the images during the test. Two key parameters including turn time and turn steps are extracted using the voxel data. Good agreement for the turn time was found for our system compared to the expert rating. The extracted numbers of turn steps are one step less than the expert rating in many test runs. The difference comes mainly from the nature of the pivot turns, and the turn time difference between the expert rating and the algorithm, namely the determination of the time duration from the beginning to the end of the turn. The development of this technology provides potential for assessing 180 degree turns in the home setting as part of a balance, stability and fall risk assessment tool.

Using Passive Sensing to Estimate Relative Energy Expenditure for Eldercare Monitoring.

This paper describes ongoing work in analyzing sensor data logged in the homes of seniors. An estimation of relative energy expenditure is computed using motion density from passive infrared motion sensors mounted in the environment. We introduce a new algorithm for detecting visitors in the home using motion sensor data and a set of fuzzy rules. The visitor algorithm, as well as a previous algorithm for identifying time-away-from-home (TAFH), are used to filter the logged motion sensor data. Thus, the energy expenditure estimate uses data collected only when the resident is home alone. Case studies are included from TigerPlace, an Aging in Place community, to illustrate how the relative energy expenditure estimate can be used to track health conditions over time.