{"id":17354,"date":"2020-01-02T07:43:53","date_gmt":"2020-01-02T06:43:53","guid":{"rendered":"https:\/\/www.inovex.de\/blog\/?p=17354"},"modified":"2022-12-02T09:11:10","modified_gmt":"2022-12-02T08:11:10","slug":"recognizing-assessing-recurrent-human-activity-with-wearable-sensors","status":"publish","type":"post","link":"https:\/\/www.inovex.de\/de\/blog\/recognizing-assessing-recurrent-human-activity-with-wearable-sensors\/","title":{"rendered":"Recognizing & Assessing Recurrent Human Activity with Wearable Sensors"},"content":{"rendered":"

The visions created by the Internet of Things<\/em>, which encompass the seamless embedding of the virtual world into daily human life have become reality by now. In that context, the ongoing miniaturization of wearables, the ubiquitous availability of capable and mobile computation devices, and the fast progress within the domain of machine learning accelerated the recognition and analysis of human activity on basis of motion sensor information. Typical use cases are, e.g. controlling devices with gestures, supporting and monitoring of patients in a medical context or the tracking and estimation of physical exercises.<\/p>\n

Related to that, the following article focuses on the analysis of human motion in sports in order to not only detect and identify different physical activities, but also to analyze them regarding their quality and correctness. Therefore, a distributed sensor system called SensX which is capable of capturing and analyzing human motion is presented. Moreover, an overview across a sequential process chain for analyzing multi-dimensional time-series with algorithms of supervised machine learning is provided. Afterwards, the application of this concept is evaluated by automatically recognizing and assessing the quality of conduction of different physical exercises. Finally the performance of two different approaches for segmenting recurrent motion events is examined.<\/p>\n

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