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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. © 2014 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

This contribution presents a corpus of spatial descriptions and describes the development of a human-driven spatial language robot system for their comprehension. The domain of application is an eldercare setting in which an assistive robot is asked to 'fetch' an object for an elderly resident based on a natural language spatial description given by the resident. In Part One, we describe a corpus of naturally occurring descriptions elicited from a group of older adults within a virtual 3D home that simulates the eldercare setting. We contrast descriptions elicited when participants offered descriptions to a human versus robot avatar, and under instructions to tell the addressee how to find the target versus where the target is. We summarize the key features of the spatial descriptions, including their dynamic versus static nature and the perspective adopted by the speaker. In Part Two, we discuss critical cognitive and perceptual processing capabilities necessary for the robot to establish a common ground with the human user and perform the 'fetch' task. Based on the collected corpus, we focus here on resolving the perspective ambiguity and recognizing furniture items used as landmarks in the descriptions. Taken together, the work presented here offers the key building blocks of a robust system that takes as input natural spatial language descriptions and produces commands that drive the robot to successfully fetch objects within our eldercare scenario. [ABSTRACT FROM AUTHOR]

A system for capturing habitual, in-home gait measurements using an environmentally mounted depth camera, the Microsoft Kinect, is presented. Previous work evaluating the use of the Kinect sensor for in-home gait measurement in a lab setting has shown the potential of this approach. In this paper, a single Kinect sensor and computer were deployed in the apartments of older adults in an independent living facility for the purpose of continuous, in-home gait measurement. In addition, a monthly fall risk assessment protocol was conducted for each resident by a clinician, which included traditional tools such as the timed up a go and habitual gait speed tests. A probabilistic methodology for generating automated gait estimates over time for the residents of the apartments from the Kinect data is described, along with results from the apartments as compared to two of the traditionally measured fall risk assessment tools. Potential applications and future work are discussed. [ABSTRACT FROM PUBLISHER]

Abstract: Older adults want to age in place at home. Sensor technology has the potential to help by monitoring individuals’ health status, detecting emergency situations, and notifying health care providers. Researchers at the University of Missouri are investigating the impact of registered nurse care coordination and technology on the ability of older adults to age in place. Technology coupled with care coordination has improved clinical outcomes. This article presents an overview of the Aging in Place research, TigerPlace as a Missouri-sponsored Aging in Place facility, and the sensor technology developed to support Aging in Place.

To address an aging population, we have been investigating sensor networks for monitoring older adults in their homes. In this paper, we report ongoing work in which passive sensor networks have been installed in 17 apartments in an aging in place eldercare facility. The network under development includes simple motion sensors, video sensors, and a bed sensor that captures sleep restlessness and pulse and respiration levels. Data collection has been ongoing for over two years in some apartments. This longevity in sensor data collection is allowing us to study the data and develop algorithms for identifying alert conditions such as falls, as well as extracting typical daily activity patterns for an individual. The goal is to capture patterns representing physical and cognitive health conditions and then recognize when activity patterns begin to deviate from the norm. In doing so, we strive to provide early detection of potential problems which may lead to serious health events if left unattended. We describe the components of the network and show examples of logged sensor data with correlated references to health events. A summary is also included on the challenges encountered and the lessons learned as a result of our experiences in monitoring aging adults in their homes. [ABSTRACT FROM AUTHOR]

Presents an introduction to articles related to human-robot interaction published in this issue of the periodical "IEEE Transactions on Systems, Man, and Cybernetics--Part A: Systems and Humans."

In conversation, people often use spatial relationships to describe their environment, for example, "there is a desk in front of me and a doorway behind it," and to issue directives, for example, "go around the desk and through the doorway." Cognitive models suggest that people use these types of relative spatial concepts to perform day-to-day navigation tasks and other spatial reasoning, which in part explains the importance of spatial language and how it developed. The research investigates the use of spatial relationships to establish a natural communication mechanism between people and robots, in particular, striving for an intuitive interface that will be easy and natural for novice users.

Investigates methods for transferring assembly skills to robots by observation of a human-performed demonstrations. Use of force-based discrete states to describe the process of contact with environment; Construction of hybrid control model for sensorimotor skills; Illustration of skill demonstration.

Presents two methods of rapidly identifying contact formations from force sensor patterns, including friction and measurement uncertainty. Use of force signal instead of positions and detailed geometric models; Use of fuzzy sets to model patterns and sensor uncertainty; Neural networks to generate confidence levels; Classification techniques in the context of robot programming.

Interactive technologies are beneficial to stroke recovery as rehabilitation interventions; however, they lack evidence for use as assessment tools. Mystic Isle is a multi-planar full-body rehabilitation game developed using the Microsoft Kinect® V2. It aims to help stroke patients improve their motor function and daily activity performance and to assess the motions of the players. It is important that the assessment results generated from Mystic Isle are accurate. The Kinect V2 has been validated for tracking lower limbs and calculating gait-specific parameters. However, few studies have validated the accuracy of the Kinect® V2 skeleton model in upper-body movements. In this paper, we evaluated the spatial accuracy and measurement validity of a Kinect-based game Mystic Isle in comparison to a gold-standard optical motion capture system, the Vicon system. Thirty participants completed six trials in sitting and standing. Game data from the Kinect sensor and the Vicon system were recorded simultaneously, then filtered and sample rate synchronized. The spatial accuracy was evaluated using Pearson’s r correlation coefficient, signal to noise ratio (SNR) and 3D distance difference. Each arm-joint signal had an average correlation coefficient above 0.9 and a SNR above 5. The hip joints data had less stability and a large variation in SNR. Also, the mean 3D distance difference of joints were less than 10 centimeters. For measurement validity, the accuracy was evaluated using mean and standard error of the difference, percentage error, Pearson’s r correlation coefficient and intra-class correlation (ICC). Average errors of maximum hand extent of reach were less than 5% and the average errors of mean and maximum velocities were about 10% and less than 5%, respectively. We have demonstrated that Mystic Isle provides accurate measurement and assessment of movement relative to the Vicon system. [ABSTRACT FROM AUTHOR]

Context: Knee abduction angle (KAA), as measured by 3-dimensional marker-based motion capture systems during jump-landing tasks, has been correlated with an elevated risk of anterior cruciate ligament injury in females. Due to the high cost and inefficiency of KAA measurement with marker-based motion capture, surrogate 2-dimensional frontal plane measures have gained attention for injury risk screening. The knee-to-ankle separation ratio (KASR) and medial knee position (MKP) have been suggested as potential frontal plane surrogate measures to the KAA, but investigations into their relationship to the KAA during a bilateral drop vertical jump task are limited. Objective: To investigate the relationship between KASR and MKP to the KAA during initial contact of the bilateral drop vertical jump. Design: Descriptive. Setting: Biomechanics laboratory. Participants: A total of 18 healthy female participants (mean age: 24.1 [3.88] y, mass: 65.18 [10.34] kg, and height: 1.63 [0.06] m). Intervention: Participants completed 5 successful drop vertical jump trials measured by a Vicon marker-based motion capture system and 2 AMTI force plates. Main Outcome Measure: For each jump, KAA of the tibia relative to the femur was measured at initial contact along with the KASR and MKP calculated from planar joint center data. The coefficient of determination (r2) was used to examine the relationship between the KASR and MKP to KAA. Results: A strong linear relationship was observed between MKP and KAA (r2 =.71), as well as between KASR and KAA (r2 =.72). Conclusions: Two-dimensional frontal plane measures show strong relationships to the KAA during the bilateral drop vertical jump. [ABSTRACT FROM AUTHOR]

There is a critical need to improve quality of life for community-dwelling older adults with disabilities. Prior research has demonstrated that a smart, in-home sensor system can facilitate aging in place for older adults living in independent living apartments with care coordination support by identifying early illness and injury detection. Self-management approaches have shown positive outcomes for many client populations. Pairing the smart, in-home sensor system with a self-management intervention for community-dwelling older adults with disabilities may lead to positive outcomes. This study is a prospective, two-arm, randomized, pragmatic clinical trial to compare the effect of a technology-supported self-management intervention on disability and health-related quality of life to that of a health education control, for rural, community-dwelling older adults. Individuals randomized to the self-management study arm will receive a multidisciplinary (nursing, occupational therapist, and social work) self-management approach coupled with the smart-home sensor system. Individuals randomized to the health education study arm will receive standard health education coupled with the smart-home sensor system. The primary outcomes of disability and health-related quality of life will be assessed at baseline and post-intervention. Generalizable guidance to scale the technology-supported self-management intervention will be developed from qualitatively developed exemplar cases. This study has the potential to impact the health and well-being of rural, community-dwelling older adults with disabilities. We have overcome barriers including recruitment in a rural population and supply chain issues for the sensor system. Our team remains on track to meet our study aims. [ABSTRACT FROM AUTHOR]

Objective--To investigate continuous wavelet transformation and neural network classification of gait data for detecting forelimb lameness in horses. Animals--12 adult horses with mild forelimb lameness. Procedure--Position of the head and right forelimb foot, metacarpophalangeal (ie, fetlock), carpal, and elbow joints was determined by use of kinematic analysis before and after palmar digital nerve blocks. We obtained 8 recordings from horses without lameness, 8 with right forelimb lameness, and 8 with left forelimb lameness. Vertical and horizontal position of the head and vertical position of the foot, fetlock, carpal, and elbow joints were processed by continuous wavelet transformation. Feature vectors were created from the transformed signals and a neural network trained with data from 6 horses, which was then tested on the remaining 2 horses for each category until each horse was used twice for training and testing. Correct classification percentage (CCP) was calculated for each combination of gait signals tested. Results--Wavelet-transformed vertical position of the head and right forelimb foot had greater CCP (85%) than untransformed data (21%). Adding data from the fetlock, carpal, or elbow joints did not improve CCP over that for the head and foot alone. Conclusions and Clinical Relevance--Wavelet transformation of gait data extracts information that is important for the detection and differentiation of forelimb lameness of horses. All of the necessary information to detect lameness and differentiate the side of lameness can be obtained by observation of vertical head movement in... [ABSTRACT FROM AUTHOR]

Objective: To develop quantitative methods for the clinical interpretation of the ballistocardiogram (BCG). Methods: A closed-loop mathematical model of the cardiovascular system is proposed to theoretically simulate the mechanisms generating the BCG signal, which is then compared with the signal acquired via accelerometry on a suspended bed. Results: Simulated arterial pressure waveforms and ventricular functions are in good qualitative and quantitative agreement with those reported in the clinical literature. Simulated BCG signals exhibit the typical I, J, K, L, M, and N peaks and show good qualitative and quantitative agreement with experimental measurements. Simulated BCG signals associated with reduced contractility and increased stiffness of the left ventricle exhibit different changes that are characteristic of the specific pathological condition. Conclusion: The proposed closed-loop model captures the predominant features of BCG signals and can predict pathological changes on the basis of fundamental mechanisms in cardiovascular physiology. Significance: This paper provides a quantitative framework for the clinical interpretation of BCG signals and the optimization of BCG sensing devices. The present paper considers an average human body and can potentially be extended to include variability among individuals. [ABSTRACT FROM AUTHOR]

We propose a nonwearable hydraulic bed sensor system that is placed underneath the mattress to estimate the relative systolic blood pressure of a subject, which only differs from the actual blood pressure by a scaling and an offset factor. Two types of features are proposed to obtain the relative blood pressure, one based on the strength and the other on the morphology of the bed sensor ballistocardiogram pulses. The relative blood pressure is related to the actual by a scale and an offset factor that can be obtained through calibration. The proposed system is able to extract the relative blood pressure more accurately with a less sophisticated sensor system compared to those from the literature. We tested the system using a dataset collected from 48 subjects right after active exercises. Comparison with the ground truth obtained from the blood pressure cuff validates the promising performance of the proposed system, where the mean correlation between the estimate and the ground truth is near to 90% for the strength feature and 83% for the morphology feature. [ABSTRACT FROM AUTHOR]

A multiple instance dictionary learning approach, dictionary learning using functions of multiple instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a “heartbeat concept” that represents an individual's personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection and heartbeat characterization as a multiple instance learning problem to address the uncertainty inherent in aligning BCG signals with ground truth during training. Experimental results show that the estimated heartbeat concept obtained by DL-FUMI is an effective heartbeat prototype and achieves superior performance over comparison algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The purpose of this study was primarily to investigate if and how a depth camera could be used to identify potentially harmful hand positions at the keyboard. Three research questions were set forth: (1) How does a pianist's self-reported hand position in a questionnaire compare to their actual hand position detected by a marker-less motion capture device? (2) Is there any correlation between the amount of time spent in neutral wrist and hand positions and previous or current injury? (3) Do advanced-level classical pianists exhibit certain characteristics in their hand and wrist playing postures that could be modeled by novice pianists? Data was collected from 14 participants who were asked to complete a self-assessment questionnaire and perform short musical excerpts, which were captured using the Microsoft Kinect Depth Camera. The results demonstrate promise in the use of the depth camera for assessing injury risk related to hand misalignment. Pianists' self-perception of their hand position often mismatched with the objective analysis. Although most pianists indicated that they experience discomfort or pain when practicing, the relationship between the amount of time spent in neutral position and the discomfort rate varied. More experienced pianists spend more time in neutral position than less experienced pianists. In addition to this study, the article gives practical strategies for assisting students with improving their self-awareness of their hand alignment at the keyboard [ABSTRACT FROM AUTHOR]

We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time–frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector. [ABSTRACT FROM PUBLISHER]

The article presents a study that examined a passive sensor data interface, used to monitor activity levels of elders in an independent living setting, which aims to improve nursing and patient outcomes based on human factors. Based on initial heuristic evaluation, the residents, family members, nurses, and physicians were asked to complete three scenarios. The results showed that users found the sensor data interface useful for identifying changing resident activities.

This study aims to explore older adults' privacy considerations for technology based monitoring applications in eldercare that use video systems. It specifically aims to introduce alternative vision based tools and identify whether distorting or "anonymizing" captured images affect older adults' privacy concerns and willingness to allow such an application to be installed in their residence. Ten residents of an independent retirement community were recruited to participate in a series of scenarios. Each scenario involved a daily activity such as sitting in the living room and having a visitor, or preparing a snack. These sessions were video-recorded using different image processing and extraction approaches. Follow-up in-depth interviews with participants were conducted after a demonstration of the captured images. Findings indicate that shape extraction can alleviate privacy concerns associated with the use of cameras. Participants expressed no privacy concerns with silhouette images and emphasized the importance of anonymity in the video sequences. They furthermore expressed the desire to control system operation by being able to turn a vision-based system off and on, and also determine who has access to the collected information. [ABSTRACT FROM AUTHOR]

Background At present, the vast majority of older adults reside in the community. Though many older adults live in their own homes, increasing numbers are choosing continuing care retirement communities (CCRCs),which range from independent apartments to assisted living and skilled-nursing facilities. With predictions of a large increase in the segment of the population aged 65 and older, a subsequent increase in demand on CCRCs can be anticipated.With these expectations, researchers have begun exploring the use of smart home information-based technologies in these care facilities to enhance resident quality of life and safety, but little evaluation research exists on older adults' acceptance and use of these technologies. Objective This study investigated the factors that influence the willingness of older adults living in independent and assisted living CCRCs to adopt smart home technology. Subjects and setting Participants (n = 14) were recruited from community-dwelling older adults, aged 65 or older, living in one of two mid-western US CCRC facilities (independent living and assisted living type facilities). Methods This study used a qualitative, descriptive approach, guided by principles of grounded theory research. Data saturation (or when no new themes or issues emerged from group sessions) occurred after four focus groups (n = 11 unique respondents) and was confirmed through additional individual interviews (n = 3). Results The findings from this study indicate that although privacy can be a barrier for older adults' adoption of smart home technology their own perception of their need for the technology can override their privacy concerns. Conclusions Factors influencing self-perception of need for smart home technology, including the influence of primary care providers, are presented. Further exploration of the factors influencing older adults' perceptions of smart home technology need and the development of appropriate interventions is necessary. [ABSTRACT FROM AUTHOR]

Background At present, the vast majority of older adults reside in the community. Though many older adults live in their own homes, increasing numbers are choosing continuing care retirement communities (CCRCs),which range from independent apartments to assisted living and skilled-nursing facilities. With predictions of a large increase in the segment of the population aged 65 and older, a subsequent increase in demand on CCRCs can be anticipated.With these expectations, researchers have begun exploring the use of smart home information-based technologies in these care facilities to enhance resident quality of life and safety, but little evaluation research exists on older adults' acceptance and use of these technologies. Objective This study investigated the factors that influence the willingness of older adults living in independent and assisted living CCRCs to adopt smart home technology. Subjects and setting Participants (n = 14) were recruited from community-dwelling older adults, aged 65 or older, living in one of two mid-western US CCRC facilities (independent living and assisted living type facilities). Methods This study used a qualitative, descriptive approach, guided by principles of grounded theory research. Data saturation (or when no new themes or issues emerged from group sessions) occurred after four focus groups (n = 11 unique respondents) and was confirmed through additional individual interviews (n = 3). Results The findings from this study indicate that although privacy can be a barrier for older adults' adoption of smart home technology their own perception of their need for the technology can override their privacy concerns. Conclusions Factors influencing self-perception of need for smart home technology, including the influence of primary care providers, are presented. Further exploration of the factors influencing older adults' perceptions of smart home technology need and the development of appropriate interventions is necessary. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a participatory evaluation of an actual "smart home" project implemented in an independent retirement facility. Using the participatory evaluation process, residents guided the research team through development and implementation of the initial phase of a smart home project designed to assist residents to remain functionally independent and age in place. We recruited nine residents who provided permission to install the technology in their apartments. We conducted a total of 75 interviews and three observational sessions. Residents expressed overall positive perceptions of the sensor technologies and did not feel that these interfered with their daily activities. The process of adoption and acceptance of the sensors included three phases, familiarization, adjustment and curiosity, and full integration. Residents did not express privacy concerns. They provided detailed feedback and suggestions that were integrated into the redesign of the system. They also reported a sense of control resulting from their active involvement in the evaluation process. Observational sessions confirmed that the sensors were not noticeable and residents did not change their routines. The participatory evaluation approach not only empowers end-users but it also allows for the implementation of smart home systems that address residents' needs. [ABSTRACT FROM AUTHOR]

Primary objective : The study aim is to explore the perceptions and expectations of seniors in regard to 'smart home' technology installed and operated in their homes with the purpose of improving their quality of life and/or monitoring their health status. Research design and methods : Three focus group sessions were conducted within this pilot study to assess older adults' perceptions of the technology and ways they believe technology can improve their daily lives. Themes discussed in these groups included participants' perceptions of the usefulness of devices and sensors in health-related issues such as preventing or detecting falls, assisting with visual or hearing impairments, improving mobility, reducing isolation, managing medications, and monitoring of physiological parameters. The audiotapes were transcribed and a content analysis was performed. Results : A total of 15 older adults participated in three focus group sessions. Areas where advanced technologies would benefit older adult residents included emergency help, prevention and detection of falls, monitoring of physiological parameters, etc. Concerns were expressed about the user-friendliness of the devices, lack of human response and the need for training tailored to older learners. Conclusions : All participants had an overall positive attitude towards devices and sensors that can be installed in their homes in order to enhance their lives. [ABSTRACT FROM AUTHOR]

Presents a missing funding acknowledgment for Dr. Mihail Popescu in the above named paper. [ABSTRACT FROM AUTHOR]