Since older adults are prone to functional decline, using Inertial-Measurement-Units (IMU) for mobility assessment score prediction gives valuable information to physicians to diagnose changes in mobility and physical performance at an early stage and increases the chances of rehabilitation. This research introduces an approach for predicting the score of the Timed Up & Go test and Short-Physical-Performance-Battery assessment using IMU data and deep neural networks. The approach is validated on real-world data of a cohort of 20 frail or (pre-) frail older adults of an average of 84.7 years. The deep neural networks achieve an accuracy of about 95% for both tests for participants known by the network.

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Abnormal behaviour can be an indicator for a medical condition in older adults. Our novel unsupervised statistical concept drift detection approach uses variational autoencoders for estimating the parameters for a statistical hypothesis test for abnormal days. As feature, the Kullback–Leibler divergence of activity probability maps derived from power and motion sensors were used. We showed the general feasibility (min. F1-Score of 91 %) on an artificial dataset of four concept drift types. Then we applied our new method to our real–world dataset collected from the homes of 20 (pre–)frail older adults (avg. age 84.75 y). Our method was able to find abnormal days when a participant suffered from severe medical condition.

This article introduce the architecture of a Long-Short-Term-Memory network for classifying transportation-modes via smartphone data and evaluates its accuracy. By using a Long-Short-Term-Memory with common preprocessing steps such as normalisation for classification tasks an F1-Score accuracy of 63.68 % was achieved with an internal test dataset. We participated as team "GanbareAMT " in the "SHL recognition challenge".

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-PhysicalPerformance-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.