Smart care using a dnn-based approach for activities of daily living (ADL) recognition

Muchun Su, Diana Wahyu Hayati, Shaowu Tseng, Jiehhaur Chen, Hsihsien Wei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including stand-ing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.

Original languageEnglish
Article number10
Pages (from-to)1-12
Number of pages12
JournalApplied Sciences (Switzerland)
Issue number1
Publication statusPublished - 1 Jan 2021


  • Activities of daily living (ADL)
  • Deep neural network (DNN)
  • Image processing
  • Pattern recognition
  • Skeletal data processing

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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