A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications

Tifenn Rault (1, 2) , Abdelmadjid Bouabdallah (2, 1) , Yacine Challal (3, 4, 1, 2) , Frédéric Marin (5, 2)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques"
3 LMCS - Laboratoire de Méthodes de Conception de Systèmes
4 CERIST - Centre de recherche sur l'Information Scientifique et Technique
5 BMBI - Biomécanique et Bioingénierie

Human context recognition (HCR) from on-body sensor networks is an important and challenging task for many healthcare applications because it offers continuous monitoring capability of both personal and environmental parameters. However, these systems still face a major energy issue that prevent their wide adoption. Indeed, in healthcare applications, sensors are used to capture data during daily life or extended stays in hospital. Thus, continuous sampling and communication tasks quickly deplete sensors' battery reserves, and frequent battery replacement are not convenient. Therefore, there is a need to develop energy-efficient solutions for long-term monitoring applications in order to foster the acceptance of these technologies by the patients. In this paper, we survey existing energy-efficient approaches designed for HCR based on wearable sensor networks. We propose a new classification of the energy-efficient mechanisms for health-related human context recognition applications and we review the related works in details. Moreover, we provide a qualitative comparison of these solutions in terms of energy-consumption, recognition accuracy and latency. Finally, we discuss open research issue and give directions for future works.

Mots clés: Energy efficient wearable sensor networks; Human context recognition

Domaines: Réseaux et télécommunications [cs.NI]

Tifenn Rault, Abdelmadjid Bouabdallah, Yacine Challal, Frédéric Marin. A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications. Pervasive and Mobile Computing, 2017, 37, pp.23-44. ⟨10.1016/j.pmcj.2016.08.003⟩. ⟨hal-01466848⟩