Abstract:

Objective To establish an algorithm to quantitatively evaluate the lower limb motor ability.Methods From August, 2016 to March, 2017, 40 subjects were divided into young healthy group ( n = 20), middle-aged group (n = 10) and elderly group (n = 10). The gait video, knee angle and ground reaction force of the subjects were collected, and the gait contour was extracted from the gait video by using the ViBe algorithm. The gait image feature was extracted by Xception-LSTM, and fused it with the knee joint angle and the ground reaction force in the feature layer. The fusion features were reduced in dimension by kernel principal component analysis, and the gait ability score (GAS) was established. All the subjects were assessed with Wisconsin Gait Scale (WGS).Results GAS was less in middle-aged group and elderly group than in the young healthy group (t > 4.164, P < 0.01), and was less in the elderly group than in the middle-aged group ( t = 7.338, P < 0.01). GAS was negative correlated with the score of WGS ( r = -0.91, P < 0.01). Conclusion The lower limb exercise ability could be quantified with GAS, which may be applied in developing rehabilitation and fitting walking aids.

Key words: elderly, lower limb, motor ability, transfer learning, convolutional neural network, recurrent neural network, feature extraction