TY - JOUR
T1 - Deep neural network approach for estimating the three-dimensional human center of mass using joint angles
AU - Chebel, Elie
AU - Tunc, Burcu
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/9/20
Y1 - 2021/9/20
N2 - Human body center of mass location plays an essential role in physical therapy, especially in investigating a subject's capability to maintain balance. However, its estimation can be a very complex, costly, and time-consuming process. To overcome the complexities and reduce the hardware cost, we proposed a deep neural network model to map the measurements of body joint angles to the 3-D center of mass position. We used an inertial measurement units-based motion-capture system (Xsens MVN Awinda) to record the joint angles and center of mass positions of 22 healthy subjects. We divided the subjects into two groups and assigned them either squat or gait tasks. Then, recorded data were merged and fed to the model to increase its generalizability. We evaluated five different input combinations to assess the effect of each input on the accuracy and generalizability of the model. The accuracy and generalizability of the models were evaluated by root-mean-square errors and comparing the differences in errors for different datasets, respectively. Root-mean-square errors ranged from 4.11 mm to 18.39 mm on both training and testing datasets for different models. Besides, adding anthropometric measurements and a Boolean parameter specifying the type of motion contributed significantly to the generalizability of the model. Also, adding unnecessary joint angles had adverse effects on the network's estimations. This study showed that by using deep neural networks, the center of mass estimations could be achieved with high accuracy, and a 17 sensors motion-capture system can be replaced with only five sensors, thus reducing the cost and complexity of the process.
AB - Human body center of mass location plays an essential role in physical therapy, especially in investigating a subject's capability to maintain balance. However, its estimation can be a very complex, costly, and time-consuming process. To overcome the complexities and reduce the hardware cost, we proposed a deep neural network model to map the measurements of body joint angles to the 3-D center of mass position. We used an inertial measurement units-based motion-capture system (Xsens MVN Awinda) to record the joint angles and center of mass positions of 22 healthy subjects. We divided the subjects into two groups and assigned them either squat or gait tasks. Then, recorded data were merged and fed to the model to increase its generalizability. We evaluated five different input combinations to assess the effect of each input on the accuracy and generalizability of the model. The accuracy and generalizability of the models were evaluated by root-mean-square errors and comparing the differences in errors for different datasets, respectively. Root-mean-square errors ranged from 4.11 mm to 18.39 mm on both training and testing datasets for different models. Besides, adding anthropometric measurements and a Boolean parameter specifying the type of motion contributed significantly to the generalizability of the model. Also, adding unnecessary joint angles had adverse effects on the network's estimations. This study showed that by using deep neural networks, the center of mass estimations could be achieved with high accuracy, and a 17 sensors motion-capture system can be replaced with only five sensors, thus reducing the cost and complexity of the process.
KW - Deep neural networks
KW - Gait
KW - Mapping
KW - Squat
KW - The center of mass estimation
UR - http://www.scopus.com/inward/record.url?scp=85111319938&partnerID=8YFLogxK
U2 - 10.1016/j.jbiomech.2021.110648
DO - 10.1016/j.jbiomech.2021.110648
M3 - Article
C2 - 34333241
AN - SCOPUS:85111319938
SN - 0021-9290
VL - 126
JO - Journal of Biomechanics
JF - Journal of Biomechanics
M1 - 110648
ER -