Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments.

Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and fall history. 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). These geriatric assessments are collected by staff nurses regularly in senior care facilities. From the GAITRite assessments on the residents, we included the Functional Ambulatory Profile (FAP) scores and gait speed to predict fall risk. We used the SHAP (SHapley Additive exPlanations) approach to explain our model predictions to understand which predictor variables contributed to increase or decrease the fall risk for an individual prediction. In case of a high fall risk prediction, predictor variables that contributed the most to elevate the risk could be further examined by the health providers for more personalized health interventions. We used the geriatric assessments, GAITRite measurements, and fall history data collected from 92 older adult residents (age = 86.2 ± 6.4, female = 57) to train machine learning models to predict 6-month fall risk. Our models predicted a 6-month fall with an AUC of 0.80 (95% CI of 0.76-0.85), sensitivity of 0.82 (95% CI of 0.74-0.89), specificity of 0.72 (95% CI of 0.67-0.76), F1 score of 0.76 (95% CI of 0.72-0.79), and accuracy of 0.75 (95% CI of 0.72-0.79). These results show that our early fall risk prediction method performs well in identifying residents who are at higher fall risk, which offers care providers and family members valuable time to perform preventive actions.

Novel walking pole gait pattern improves activity in an older adult with chronic low back pain.

A 65-year-old woman with chronic low back pain participated in a 1-week community walking poles course. Although the participant received instruction in the standard Nordic walking method, she independently adopted a novel, modified, two-point gait pattern. Subsequently, her pain and activity tolerance using walking poles were monitored at 6 and 12 months. The participant ambulated two times the distance and reported lower ratings of perceived exertion and pain at 6-month and 12month follow-ups when walking with poles compared with walking without poles. This case highlights the potential effect of respecting patient preference within the clinical decision-making model. Doing so empowered a participant with chronic low back pain to adopt a novel, self-selected gait pattern and improve her short-term and long-term outcomes associated with chronic musculoskeletal disease.

Caregivers' Experiences Regarding Training and Support in the Post-Acute Home Health-Care Setting.

BACKGROUND: Post-acute home health-care (HHC) services provide a unique opportunity to train and support family caregivers of older adults returning home after a hospitalization. To enhance family-focused training and support strategies, we must first understand caregivers' experiences. OBJECTIVE: To explore caregivers' experiences regarding training and support for managing older adults' physical functioning (PF) needs in the post-acute HHC setting. METHOD: We conducted a qualitative descriptive study using semi-structured telephone interviews of 20 family caregivers. Interviews were recorded, transcribed, and analyzed using conventional content analysis. RESULTS: We identified the following primary categories: facilitators to learning (eg, past experience, learning methods), barriers to learning (eg, learning on their own, communication, timing/logistics, preferred information and timing of information delivery), and interactions with HHC providers (eg, positive/negative interactions, provider training and knowledge). CONCLUSION: Caregivers were responsive to learning strategies to manage older adults' PF needs and, importantly, voiced ideas to improve family-focused training and support. HHC providers can use these findings to tailor training and support of family caregivers in the post-acute HHC setting.

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.

Caregivers' Perceptions Managing Functional Needs Among Older Adults Receiving Post-Acute Home Health Care.

Caregivers play important roles in managing the physical functioning (PF) needs of older adults transitioning home after a hospitalization. Training and support of caregivers in the post-acute home health care (HHC) setting should incorporate caregivers' perspectives. To explore caregivers' experiences managing PF needs in the post-acute HHC setting, semi-structured telephone interviews of 20 caregivers were conducted. Conventional content analysis revealed patient-, caregiving task-, caregiver-, and home environment-related themes consistent with the Theory of Dependent Care. Caregivers highlighted the dynamics and contributors of PF needs for older patients in the post-acute HHC setting and depicted the enormity of caregiving tasks needed to manage older patients' PF needs. Caregivers also described their perceived roles and challenges in managing PF deficits, including a sense of isolation when they were the sole caregiver. Findings from this research can guide nursing efforts to target caregiver training and support during this critical care transition. [Res Gerontol Nurs. 2019; 12(4):174-183.].

Using Embedded Sensors in Independent Living to Predict Gait Changes and Falls.

This study explored using Big Data, totaling 66 terabytes over 10 years, captured from sensor systems installed in independent living apartments to predict falls from pre-fall changes in residents' Kinect-recorded gait parameters. Over a period of 3 to 48 months, we analyzed gait parameters continuously collected for residents who actually fell ( n = 13) and those who did not fall ( n = 10). We analyzed associations between participants' fall events ( n = 69) and pre-fall changes in in-home gait speed and stride length ( n = 2,070). Preliminary results indicate that a cumulative change in speed over time is associated with the probability of a fall ( p < .0001). The odds of a resident falling within 3 weeks after a cumulative change of 2.54 cm/s is 4.22 times the odds of a resident falling within 3 weeks after no change in in-home gait speed. Results demonstrate using sensors to measure in-home gait parameters associated with the occurrence of future falls.

Evaluation of the microsoft kinect skeletal versus depth data analysis for timed-up and go and figure of 8 walk tests.

We compared the performance of the Kinect skeletal data with the Kinect depth data in capturing different gait parameters during the Timed-up and Go Test (TUG) and Figure of 8 Walk Test (F8W). The gait parameters considered were stride length, stride time, and walking speed for the TUG, and number of steps and completion time for the F8W. A marker-based Vicon motion capture system was used for the ground-truth measurements. Five healthy participants were recruited for the experiment and were asked to perform three trials of each task. Results show that depth data analysis yields stride length and stride time measures with significantly low percentile errors as compared to the skeletal data analysis. However, the skeletal and depth data performed similar with less than 3% of absolute mean percentile error in determining the walking speed for the TUG and both parameters of F8W. The results show potential capabilities of Kinect depth data analysis in computing many gait parameters, whereas, the Kinect skeletal data can also be used for walking speed in TUG and F8W gait parameters.

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.

Development and preliminary validation of an interactive remote physical therapy system.

In this paper, we present an interactive physical therapy system (IPTS) for remote quantitative assessment of clients in the home. The system consists of two different interactive interfaces connected through a network, for a real-time low latency video conference using audio, video, skeletal, and depth data streams from a Microsoft Kinect. To test the potential of IPTS, experiments were conducted with 5 independent living senior subjects in Kansas City, MO. Also, experiments were conducted in the lab to validate the real-time biomechanical measures calculated using the skeletal data from the Microsoft Xbox 360 Kinect and Microsoft Xbox One Kinect, with ground truth data from a Vicon motion capture system. Good agreements were found in the validation tests. The results show potential capabilities of the IPTS system to provide remote physical therapy to clients, especially older adults, who may find it difficult to visit the clinic.

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.

Sit-to-stand measurement for in-home monitoring using voxel analysis.

We present algorithms to segment the activities of sitting and standing, and identify the regions of sit-to-stand (STS) transitions in a given image sequence. As a means of fall risk assessment, we propose methods to measure STS time using the 3-D modeling of a human body in voxel space as well as ellipse fitting algorithms and image features to capture orientation of the body. The proposed algorithms were tested on ten older adults with ages ranging from 83 to 97. Two techniques in combination yielded the best results, namely the voxel height in conjunction with the ellipse fit. Accurate STS time was computed on various STSs and verified using a marker-based motion capture system. This application can be used as part of a continuous video monitoring system in the homes of older adults and can provide valuable information to help detect fall risk and enable early interventions.

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 measurement of the effect of strategically weighted vests on ambulation.

Strategically weighted vests are currently being used to treat patients with Parkinson's, Multiple Sclerosis, and ataxia. While studies have been conducted to demonstrate the effectiveness of these vests, there has been very little research into the mechanisms that give rise to the vest's results. This study demonstrates the ability to capture gait parameters from depth images[1] in the home with sufficient sensitivity to support future investigation of the weighted vest intervention. The study also explores multiple metrics, using in-home gait sensing, to study a subject's ambulatory ability including gait mechanics, uncertainty in motion, and gait cadence. We then investigate the effects of these vests on a subject's ambulation by examining these metrics both before and after the vest is worn. While only four subjects were used, results are promising, showing a statistically significant and clinically significant change in many of these metrics as a result of the vest. The cases presented here concern two subjects, one with a "tight" gait caused by Progressive Supranuclear Palsy, and the second with an excessively "loose" gait due to Parkinson's disease. We show that in both subjects, using the vest immediately moved the metrics in a direction beneficial to the subject's clinical condition. This result concurs with clinical observations as measured using various clinical fall risk instruments.

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.

Radar walking speed measurements of seniors in their apartments: technology for fall prevention.

Falls are a significant cause of injury and accidental death among persons over the age of 65. Gait velocity is one of the parameters which have been correlated to the risk of falling. We aim to build a system which monitors gait in seniors and reports any changes to caregivers, who can then perform a clinical assessment and perform corrective and preventative actions to reduce the likelihood of falls. In this paper, we deploy a Doppler radar-based gait measurement system into the apartments of thirteen seniors. In scripted walks, we show the system measures gait velocity with a mean error of 14.5% compared to the time recorded by a clinician. With a calibration factor, the mean error is reduced to 10.5%. The radar is a promising sensing technology for gait velocity in a day-to-day senior living environment.

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.

Body sway measurement for fall risk assessment using inexpensive webcams.

In this paper, we present a method for extracting body sway parameters from a three-dimensional voxel reconstruction, which is built using silhouettes captured from two calibrated web camera views. The results were validated with a Vicon motion capture system. Experiments were conducted in which subjects stand and sway in the anterior-posterior direction and then in the lateral directions with two different frequencies. In addition, experiments were also conducted where subjects walked in a straight path at different speeds. Through the experiment, the Vicon cameras recorded the motion of reflective markers attached to subjects, and our two calibrated cameras captured the images. Good agreement was found with our system compared to the Vicon results, given the limitation of voxel space resolution and frame rate. The development of this technology provides potential capability of measuring body sway in daily living environment for elderly people, and can be used as part of a balance, stability and fall risk assessment tool.

Smart Carpet: Developing a sensor system to detect falls and summon assistance.

Cognitive deficits experienced by older adults with dementia limit use of wearable devices (necklaces or bracelets) that summon assistance after the older adult falls. To use these wearable devices, older adults must choose to wear them, remember how to use them, and be conscious after falling. Devices such as the Smart Carpet substitute pre-programmed or automatic functions for functions requiring deliberation and decision. After development of a Smart Carpet prototype, 11 volunteers participated in tests to measure sensitivity of sensors embedded in the Smart Carpet. The embedded sensors were not perceptible to the volunteers as they walked across the Smart Carpet and successfully detected gait characteristics. Findings confirmed the feasibility of fall detection. Measurements obtained of gait characteristics will be used in development of more advanced versions of the Smart Carpet.

Dance-based therapy in a program of all-inclusive care for the elderly: an integrative approach to decrease fall risk.

BACKGROUND: Loss of balance and diminished gait are major fall risk factors in older persons. Literature suggests that physical activity based on dance may improve balance and gait. The aim of this pilot study was to determine whether dance-based therapy affects the balance/gait of community-based frail seniors. CONCEPTUAL FRAMEWORK: The Roy Adaptation Model and Environmental Press Theory were used as joint frameworks. PARTICIPANTS: Eleven participants were recruited from a Program of All-inclusive Care for the Elderly (PACE). Inclusion criteria were (1) Mini-Mental State Examination score of 23 or more, (2) attending PACE on Monday, Wednesday, and Friday, and (3) able to stand with or without assistance. METHODS: A Lebed Method dance intervention was conducted using a longitudinal design 3 times a week for 6 weeks. Functional Reach and Timed Get Up and Go were measured at baseline, 6 weeks after the start of the intervention, and 6 weeks postintervention and repeated to estimate the persistence of the effect. CONCLUSIONS: Graphs were compared looking for functional trends; postintervention interviews were conducted with each subject. IMPLICATIONS: Dance therapy results in positive functional trends, suggesting that further study using dance-based therapy will be useful to decrease fall risks in older persons.

An analysis of human motion detection systems use during elder exercise routines.

Human motion analysis provides motion pattern and body pose estimations. This study integrates computer-vision techniques and explores a markerless human motion analysis system. Using human-computer interaction (HCI) methods and goals, researchers use a computer interface to provide feedback about range of motion to users. A total of 35 adults aged 65 and older perform three exercises in a public gym while human motion capture methods are used. Following exercises, participants are shown processed human motion images captured during exercises on a customized interface. Standardized questionnaires are used to elicit responses from users during interactions with the interface. A matrix of HCI goals (effectiveness, efficiency, and user satisfaction) and emerging themes are used to describe interactions. Sixteen users state the interface would be useful, but not necessarily for safety purposes. Users want better image quality, when expectations are matched satisfaction increases, and unclear meaning of motion measures decreases satisfaction.