Qualitative assessment of recurrent human motion

Abstract

Abstract-Smartphone applications designed to track human motion in combination with wearable sensors, e.g., during physical exercising, raised huge attention recently. Commonly, they provide quantitative services, such as personalized training instructions or the counting of distances. But qualitative monitoring and assessment is still missing, e.g., to detect malpositions, to prevent injuries, or to optimize training success.We address this issue by presenting a concept for qualitative as well as generic assessment of recurrent human motion by processing multi-dimensional, continuous time series tracked with motion sensors. Therefore, our segmentation procedure extracts individual events of specific length and we propose expressive features to accomplish a qualitative motion assessment by supervised classification. We verified our approach within a comprehensive study encompassing 27 athletes undertaking different body weight exercises. We are able to recognize six different exercise types with a success rate of 100% and to assess them qualitatively with an average success rate of 99.3%.

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

“…However, the number of exercise executions that can be used to create rules/templates is limited. This reduces the scalability of these approaches [29]. Consequently, it is difficult to develop personalized solutions that can match the unique characteristics or impairments of each individual.…”

“…Of the 88 publications, 29 applied a filter to their data (Table 8), with 16 in the healthcare domain, eight in the sports domain, and five in the wellness domain. The most common filter was the Butterworth filter, which was used in 18 publications (16 lowpass [29], [30], [32], [52], [54], [68], [69], [73], [76], [86], [105], [107], [108], [109], [112], [113], one high-pass [84], and one band-pass [50]), and then the moving average used in five publications [46], [92], [93], [94], [95]. Filters are most commonly applied to data captured from inertial sensors, with 14/39 publications that used inertial sensors applying them [29], [32], [50], [52], [68], [69], [73], [76], [102], [103], [109], [112], [113], [119].…”

IntelliRehabDS (IRDS)—A Dataset of Physical Rehabilitation Movements

“…Some of the ideas rely on using sensors attached to the body. In [11], the authors gathered a dataset using five sensor devices attached to the ankles, wrists and chest in order to record six exercises performed by 27 athletes and to label the data with a qualitative rating from one to five.…”

The importance of software development for the monitoring of training to high competition

“…No artigo [34] é abordado o uso de sensores na roupa e a sua interligação com aplicativos para smartphone, de forma a rastrear o movimento do individuo durante o exercício físico abordando a questão da detecção do mau posicionamento, prevenção de lesões ou otimizar o sucesso do treinamento.…”