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A continuous monitoring of the physical strength and mobility of elderly people is important for maintaining their health and treating diseases at an early stage. However, frequent screenings by physicians are exceeding the logistic capacities. An alternate approach is the automatic and unobtrusive collection of functional measures by ambient sensors. In the current publication, we show the correlation among data of ambient motion sensors and the well-established mobility assessments Short-Physical-Performance-Battery, Tinetti and Timed Up & Go. We use the average number of motion sensor events as activity measure for correlation with the assessment scores. The evaluation on a real-world dataset shows a moderate to strong correlation with the scores of standardised geriatrics physical assessments., 15 pages, 1 figure, 16 tables

Background It is important to identify the relevant parameters of physical performance to prevent early functional decline and to prolong independent living. The aim of this study is to describe the development of physical performance in a healthy community-dwelling older cohort aged 70+ years using comprehensive assessment over two years and to subsequently identify the most relevant predictive tests for physical decline to minimize assessment. Methods Physical performance was measured by comprehensive geriatric assessment. Predictors for the individual decline of physical performance by Principal Component and k-means Cluster Analysis were developed, and sensitivity and specificity determined accordingly. Results 251 subjects (Ø 75.4 years) participated in the study. Handgrip strength was low in 21.1%. The follow-up results of tests were divergent. Handgrip strength [− 16.95 (SD 11.55)] and the stair climb power test (power) [− 9.15 (SD 16.84)] yielded the highest percentage changes. Four most relevant tests (handgrip strength, stair climb power time, timed up & go and 4-m gait speed) were identified. A predictor based on baseline data was determined (sensitivity 82%, specificity 96%) to identify subjects characterized by a high degree of physical decline within two years. Discussion Although the cohort of older adults is heterogeneous, most of the individuals in the study exhibited high levels of physical performance; only a few subjects suffered a relevant decline within the 2-year follow-up. Four most relevant tests were identified to predict relevant decline of physical function. Conclusion In spite of ceiling effects of the geriatric assessment in high-performers, we assume that it is possible to predict an individual’s risk of physical decline within 2 years with four tests of a comprehensive geriatric assessment.

Dyslexia is a widespread specific learning disorder, which can have a particularly negative influence on the learning success of children. Early detection of dyslexia is the foundation for early intervention, which is the key to reduce the adverse effects of dyslexia, e.g., bad school grades. In this paper, we present the prototype of a puzzle app, which we explicitly designed with the human-centred design (HCD) process for dyslexia screening in pre-reader using new indicators related to motor skills to ensure users needs for the data collection to apply machine learning prediction. The app records the telemetry of the gaming sequence in order to derive future statements about the prevalence of dyslexia based on the telemetry data. The high-fidelity prototype was evaluated with a five-user test usability study with five German-speaking child-parent pairs. The results show how young children and parents are interacting with new games, and how new applications (web and mobile technologies) which are used for online experiments, could be developed. The usability of the prototype is suitable for the target group with only minor limitations.

The Ovako Working posture Assessment System (OWAS) is a commonly used observational assessment method for determining the risk of work-related musculoskeletal disorders. OWAS claims to be suitable in the application for untrained persons but there is not enough evidence for this assumption. In this paper, inter-rater (inter-observer) reliability (agreement) is examined down to the level of individual postures and categories. For this purpose, the postures of 20 volunteers have been observed by 3 varying human raters in a laboratory setting and the inter-rater agreement against reference values was determined. A high agreement of over 98%(κ=0.98) was found for the postures of the arms but lower agreements were found for posture classification of the legs (66-97%,κ=0.85) and the upper body (80-96%,κ=0.85). No significant difference was found between raters with and without intense prior training in physical therapy. Consequently, the results confirm the general reliability of the OWAS method especially for raters with non-specialized background but suggests weaknesses in the reliable detection of a few particular postures.

Indicators for a decrement in vigilance are a slowdown in reaction times and an increase in alpha power in the electroencephalogram in posterior regions of the brain. Transcranial alternating current stimulation (tACS) is a neuropsychological technique that has been found to interact with intrinsic brain oscillations and is able to enhance cognitive and behavioral performance. Recent studies show that tACS in the gamma frequency range (30–80 Hz) is able to downregulate amplitudes in the alpha frequency range (8–12 Hz), in accordance to the effect referred to as cross-frequency coupling, where intrinsic alpha and gamma waves modulate each other. We applied 40 Hz gamma-tACS to the visual cortex during a vigilance experiment and investigated if stimulation improves reaction times and error rates with time-on-task. In our sham controlled experiment, participants completed two blocks of 30 minutes duration while performing the same visual two-choice task. The first block was used as BASELINE. A statistical analysis with a linear mixed model revealed a significantly lower increase of modeled reaction times over time in the INTERVENTION-block of the tACS-group as compared with their BASELINE-block whereas there was no significant change between the BASELINE- and INTERVENTION-block for the SHAM-group. Error rates did not differ between groups. This paper indicates that gamma-tACS can enhance performance in vigilance tasks as it significantly decreased the slowdown of reaction times in our study.

This paper presents a new mobile near-infrared functional spectroscopy (fNIRS) device, with digital detectors that can be placed anywhere on the head and fit into standard caps to measure cortical brain activation. The device's functionality was evaluated in two steps, i.e. first, by means of simple pulse measurements and second, in a motor cortex study with nine subjects. In this study, the subjects had to alternate between right and left hands while using hand-held strength trainers. While the signals from the mobile prototype were not yet stable enough across all channels to perform analysis such as statistical parametric mapping, it was able to measure significant brain activation changes over the area of the motor cortex with the mobile prototype when the contralateral hand was activated in four subjects. In contrast, the device was yet unable to measure ipsilateral activities. The problems encountered and possible methods to improve signal acquisition are discussed at the end of the paper.

Cardiopulmonary resuscitation (CPR) is alongside electrical defibrillation the most crucial countermeasure for sudden cardiac arrest, which affects thousands of individuals every year. In this paper, we present a novel approach including sinusoid models that use skeletal motion data from an RGB-D (Kinect) sensor and the Differential Evolution (DE) optimization algorithm to dynamically fit sinusoidal curves to derive frequency and depth parameters for cardiopulmonary resuscitation training. It is intended to be part of a robust and easy-to-use feedback system for CPR training, allowing its use for unsupervised training. The accuracy of this DE-based approach is evaluated in comparison with data of 28 participants recorded by a state-of-the-art training mannequin. We optimized the DE algorithm hyperparameters and showed that with these optimized parameters the frequency of the CPR is recognized with a median error of $\pm 2.9$ compressions per minute compared to the reference training mannequin., Final paper, 22 pages

To initiate appropriate interventions and avoid physical decline, comprehensive measurements are needed to detect functional changes in elderly people at the earliest possible stage. The established Timed UpGo (TUG) test takes little time and, due to its standardized and easy procedure, can be conducted by elderly people in their own homes without clinical guidance. Therefore, cheap light barriers (LBs) and force sensors (FSs) are well suited ambient sensors that could easily be attached to existing (arm)chairs to measure and report TUG times in order to identify functional decline. We validated the sensitivity of these sensors in a clinical trial with 100 elderlies aged 58-92 years with a mean of 74 (±6.78) years by comparing the sensor-based results with standard TUG measurements using a stopwatch. We further evaluated the accuracy enhancement when calibrating the algorithm via a mixed linear model. With calibration, the LBs achieved a root mean square error (RMSE) of 0.83 s, compared to 1.90 s without, and the FSs achieved 0.90 s compared to 2.12 s without. The suitability of measuring accurate TUG times with each of the ambient sensors and of measuring TUG regularly in the homes of elderly people could be confirmed.

Gait analysis is often supported by technology. Due to limitations in optical systems, such as limited measurement volumes and the requirement of a laboratory environment, low-cost inertial measurement unit (IMU) based motion capture systems might be better suited for gait analysis since they involve no spatial limitations and are flexibly applicable. In this paper, we investigate if low-cost IMU-based motion capture suits are an adequate alternative for clinical gait analysis in terms of accuracy of the determination of joint flexions and gait parameters. For this reason, we developed a gait analysis system and a gait analysis algorithm, which detects joint positions based on the Joint Coordinate System and determines knee, hip, and ankle flexions, as well as spatiotemporal parameters such as the number of steps, cadence, step duration and step length, and the specific gait phases. We evaluated and validated the IMU-based system in comparison to camera-based measurements (as gold standard) with three different healthy adult subjects. The evaluation indicates that the full-body motion capture system achieves a high degree of precision (0.86) and recall (0.98) in the recognition of gait cycles. The harmonic mean F0.15 of the two factors precision and recall is on average 0.96 and the mentioned temporal gait parameters can be determined with an error below 10 ms. The mean deviation in the determination of joint angles amounts 1.35° ± 2°. Consequently, the article at hand indicates that low-cost IMU-based motion capture suits are an accurate alternative for gait analysis.

This article introduces a gesture-based user interface for hospital beds. This interface enables caregivers to focus on their patients and have both hands available for mobilizing and transferring them. Gestures are detected either via static (96% sensitivity) or dynamic gestures (67.5% sensitivity) and might be corrected by an extra repetition. Once gestures are correctly detected, caregivers can trigger bed movements via a foot switch as a hands-free operation, which as well functions as a dead man button. The evaluation of the usability through interviews with caregivers highlighted the system’s general applicability, but as well some future challenges that have to be solved in order to achieve a system for every-day use.

In the aging sectors of societies in the western world, dementia and its characteristics such as disorientation and obliviousness are becoming a significant problem to an increasing number of people and health systems. In order to enable such dementia patients to regain a self-determined life, we have developed a mobile orientation system with a focus on minimal operational costs and a speech based human computer interface. This system assists dementia patients in everyday problems, such as remembering appointments and staying on track within their familiar surroundings as well as informing caretakers in critical situations.