Recognition of human activity based on sparse data collected from smartphone sensors

EasyChair Preprint 828

Abstract

This paper proposes a method of human activity monitoring based on the regular use of sparse acceleration data and GPS positioning collected during smartphone daily utilization. The application addresses, in particular, the elderly population with regular activity patterns associated with daily routines. The approach is based on the clustering of acceleration and GPS data to characterize the user's pattern activity and localization for a given period. The current activity pattern is compared to the one obtained by the learned data patterns, generating alarms of abnormal activity and unusual location. The obtained results allow to consider that the usage of the proposed method in real environments can be beneficial for activity monitoring without using complex sensor networks.

Keyphrases: Human activity monitoring, smartphone sensors, unsupervised learning

@booklet{EasyChair:828,
  author    = {João Figueiredo and Gonçalo Gordalina and Pedro Correia and Gabriel Pires and Luís Oliveira and Ricardo Martinho and Rui Rijo and Pedro Assuncao and Alexandra Seco and Rui Fonseca-Pinto},
  title     = {Recognition of human activity based on sparse data collected from smartphone sensors},
  doi       = {10.29007/vdtl},
  howpublished = {EasyChair Preprint 828},
  year      = {EasyChair, 2019}}