Activity tracking and monitoring of patients with alzheimer’s disease

Kam Yiu Lam, Nelson Wai Hung Tsang, Song Han, Wenlong Zhang, Joseph Kee Yin Ng, Ajit Nath

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


In this paper, by applying motion detection and machine learning technologies, we have designed and developed an activity tracking and monitoring system, called SmartMind, to help Alzheimer’s Disease (AD) patients to live independently within their living rooms while providing emergency assistances and supports when necessary. Allowing AD patients to handle their daily activities not only can release the burdens on their families and caregivers, it is also highly important to help them regain confidence towards a healthy life. The daily activities of a patient captured from SmartMind can also serve as an important indicator to describe his/her normal living habit (NLH). By checking NLH, the patient’s current health status can be estimated on a daily basis. In the testing experiments of SmartMind, we have demonstrated the accuracy of SmartMind in activity detection and investigated its performance when different machine learning algorithms were adopted for posture detection. The performance results indicate that both support vector machine (SVM) and naive bayes (NB) can achieve an accuracy of higher than 97 % while the random forrests (RF) only gives an accuracy of around 73 %.

Original languageEnglish (US)
Pages (from-to)489-521
Number of pages33
JournalMultimedia Tools and Applications
Issue number1
StatePublished - Jan 1 2017


  • Dementia
  • Health informatics Context-aware computing
  • Motion detection
  • Pervasive computing

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


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