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EnglishPhysical activity is strongly linked with mental and physical health in the elderly population and accurate monitoring of activities of daily living (ADLs) can help improve quality of life and well-being. This study presents and validates an inertial sensors-based physical activity classification system developed with older adults as the target population. The dataset was collected in free living conditions without placing constraints on the way and order of performing ADLs. Four sensor locations (chest, lower back, wrist, and thigh) were explored to obtain the optimal number and combination of sensors by finding the best tradeoff between the system's performance and wearability. Several feature selection techniques were implemented on the feature set obtained from acceleration and angular velocity signals to classify four major ADLs (sitting, standing, walking, and lying). Support vector machine was used for the classification of the ADLs. The findings show the potential of different solutions (single-sensor or multi-sensor) to correctly classify the ADLs of older people in free living conditions. Considering a minimal set-up of a single sensor, the sensor worn at the L5 achieved the best performance. A two-sensor solution (L5 + thigh) achieved a better performance with respect to a single-sensor solution. On the other hand, considering more than two sensors did not provide further improvements. Finally, we evaluated the computational cost of different solutions and it was shown that a feature selection step can reduce the computational cost of the system and increase the system performance in most cases. This can be helpful for real-time applications.
| Titolo: | Physical Activity Classification for Elderly People in Free Living Conditions | |
| Autore/i: | Awais M; Chiari L; Ihlen EAF; Helbostad J; Palmerini L. | |
| Autore/i Unibo: | ||
| Anno: | 2019 | |
| Rivista: | ||
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/JBHI.2018.2820179 | |
| Abstract: | Physical activity is strongly linked with mental and physical health in the elderly population and accurate monitoring of activities of daily living (ADLs) can help improve quality of life and well-being. This study presents and validates an inertial sensors-based physical activity classification system developed with older adults as the target population. The dataset was collected in free living conditions without placing constraints on the way and order of performing ADLs. Four sensor locations (chest, lower back, wrist, and thigh) were explored to obtain the optimal number and combination of sensors by finding the best tradeoff between the system's performance and wearability. Several feature selection techniques were implemented on the feature set obtained from acceleration and angular velocity signals to classify four major ADLs (sitting, standing, walking, and lying). Support vector machine was used for the classification of the ADLs. The findings show the potential of different solutions (single-sensor or multi-sensor) to correctly classify the ADLs of older people in free living conditions. Considering a minimal set-up of a single sensor, the sensor worn at the L5 achieved the best performance. A two-sensor solution (L5 + thigh) achieved a better performance with respect to a single-sensor solution. On the other hand, considering more than two sensors did not provide further improvements. Finally, we evaluated the computational cost of different solutions and it was shown that a feature selection step can reduce the computational cost of the system and increase the system performance in most cases. This can be helpful for real-time applications. | |
| Data stato definitivo: | 2019-02-27T15:33:19Z | |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipo | Licenza | |
|---|---|---|---|---|
| IEEE JBHI 2018 - cover.pdf | postprint | Postprint | Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND) | Open Access Visualizza/Apri |
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