A task-model based approach for detecting ADL-related anomalies - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

A task-model based approach for detecting ADL-related anomalies

(1) , (1) , (1) , (2, 3)
1
2
3
Jose Manuel Negrete Ramírez
  • Function : Author
  • PersonId : 1191196
Célia Martinie
Philippe Palanque

Abstract

In this paper, a task model based on HAMSTERS-XL notation is proposed to represent activities of daily living (ADL). For an efficient representation of ADL, it is also a need to delimit requirements in time and location for each ADL, as well as the sensor data that define the ADL. In this sense, it is also proposed a procedure consisting of a set of step intending to delimit such time and location requirements, regarding both activities and sensors; alongside the events that need to be identified. The presented approach aims at providing an analysis tool regarding the performance of elderly/handicapped people by comparing data recovered from sensors within a smart home environment simulator to the task models. The feed oneself ADL (as well as its possible variations) is picked in order to evaluate our proposal. After proper analysis was carried out, anomalies detected during ADL performance are pointed out in order to detect possible ADL routine modification in a coherent manner through improved task models.
Fichier principal
Vignette du fichier
A_task-model_based_approach_for_detecting_ADL-related_anomalies.pdf (708.82 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-03862889 , version 1 (21-11-2022)

Identifiers

Cite

Jose Manuel Negrete Ramírez, Célia Martinie, Philippe Palanque, Yudith Cardinale. A task-model based approach for detecting ADL-related anomalies. 10th International Workshop on e-Health Pervasive Wireless Applications and Service @ WiMob 2022, IEEE ComSoc, Oct 2022, Thessaloniki, Greece. pp.57-62, ⟨10.1109/WiMob55322.2022.9941688⟩. ⟨hal-03862889⟩
0 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More