TY - JOUR
T1 - Classical machine learning versus deep learning for the older adults free-living activity classification
AU - Awais, Muhammad
AU - Chiari, Lorenzo
AU - Ihlen, Espen A. F.
AU - Helbostad, Jorunn L.
AU - Palmerini, Luca
N1 - Data Availability Statement: The script from the ADAPT validation data set used in this study is available on request to Jorunn L. Helbostad.
Funding information: This study was partially funded by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 820820 (Mobilise-D). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This study was also partially funded by the Norwegian Research Council (FRIMEDBIO, Contract No 230435).
PY - 2021/7
Y1 - 2021/7
N2 - Physical activity has a strong influence on mental and physical health and is essential in healthy ageing and wellbeing for the ever-growing elderly population. Wearable sensors can provide a reliable and economical measure of activities of daily living (ADLs) by capturing movements through, e.g., accelerometers and gyroscopes. This study explores the potential of using classical machine learning and deep learning approaches to classify the most common ADLs: walking, sitting, standing, and lying. We validate the results on the ADAPT dataset, the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate video labelled data recorded in a free-living environment from older adults living independently. The findings suggest that both approaches can accurately classify ADLs, showing high potential in profiling ADL patterns of the elderly population in free-living conditions. In particular, both long short-term memory (LSTM) networks and Support Vector Machines combined with ReliefF feature selection performed equally well, achieving around 97% F-score in profiling ADLs.
AB - Physical activity has a strong influence on mental and physical health and is essential in healthy ageing and wellbeing for the ever-growing elderly population. Wearable sensors can provide a reliable and economical measure of activities of daily living (ADLs) by capturing movements through, e.g., accelerometers and gyroscopes. This study explores the potential of using classical machine learning and deep learning approaches to classify the most common ADLs: walking, sitting, standing, and lying. We validate the results on the ADAPT dataset, the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate video labelled data recorded in a free-living environment from older adults living independently. The findings suggest that both approaches can accurately classify ADLs, showing high potential in profiling ADL patterns of the elderly population in free-living conditions. In particular, both long short-term memory (LSTM) networks and Support Vector Machines combined with ReliefF feature selection performed equally well, achieving around 97% F-score in profiling ADLs.
KW - Classical machine learning
KW - Deep learning
KW - Free living
KW - Older adults
KW - Physical activity classification
KW - Wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85109072494&partnerID=8YFLogxK
U2 - 10.3390/s21144669
DO - 10.3390/s21144669
M3 - Article
VL - 21
JO - Sensors
JF - Sensors
SN - 1424-8220
IS - 14
M1 - 4669
ER -