Advanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People

Lisa Schrader, Agustín Vargas Toro, Sebastian Konietzny, Stefan Rüping, Barbara Schäpers, Martina Steinböck, Carmen Krewer, Friedemann Müller, Jörg Güttler, Thomas Bock

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

Ageing is associated with a decline in physical activity and a decrease in the ability to perform activities of daily living, affecting physical and mental health. Elderly people or patients could be supported by a human activity recognition (HAR) system that monitors their activity patterns and intervenes in case of change in behavior or a critical event has occurred. A HAR system could enable these people to have a more independent life. In our approach, we apply machine learning methods from the field of human activity recognition (HAR) to detect human activities. These algorithmic methods need a large database with structured datasets that contain human activities. Compared to existing data recording procedures for creating HAR datasets, we present a novel approach, since our target group comprises of elderly and diseased people, who do not possess the same physical condition as young and healthy persons. Since our targeted HAR system aims at supporting elderly and diseased people, we focus on daily activities, especially those to which clinical relevance in attributed, like hygiene activities, nutritional activities or lying positions. Therefore, we propose a methodology for capturing data with elderly and diseased people within a hospital under realistic conditions using wearable and ambient sensors. We describe how this approach is first tested with healthy people in a laboratory environment and then transferred to elderly people and patients in a hospital environment. We also describe the implementation of an activity recognition chain (ARC) that is commonly used to analyse human activity data by means of machine learning methods and aims to detect activity patterns. Finally, the results obtained so far are presented and discussed as well as remaining problems that should be addressed in future research.

Original languageEnglish
Pages (from-to)139-165
Number of pages27
JournalJournal of Population Ageing
Volume13
Issue number2
DOIs
StatePublished - 1 Jun 2020

Keywords

  • Active ageing
  • Data acquisition
  • Human activity recognition
  • Machine learning
  • Supervised learning

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