| dc.description.abstract | In this thesis, two serial studies attempted to quantify neurological and functional defects by correlating clinical assessment scales and kinematics obtained from wearable devices, including surface electromyography and inertial sensors. This thesis contains two domains. One includes classifying neurological recovery status by integrating surface electromyography and inertial sensors to classify clinical Brunnstrom stages. Then, we managed to classify the neurological level through a single inertial sensor.
The results explore that the Support Vector Machine has the best accuracy in classifying the Brunnstrom stage than k-Nearest Neighbor and the Artificial Neural Network. The overall accuracy ranges from 82.86% to 100%, while the Support Vector Machine owns an accuracy ranging from 92.5% to 100%. Additionally, after adopting the threshold values, we get a good accuracy, ranging from 86.78% to 100% among different stages, to classify the Brunnstrom stages, by using a single inertial sensor on the subjects’ feet. This system has been proven feasible for predicting the lower limb Brunnstrom stage for hemiparetic and hemiplegic patients.
The other domain correlates clinical assessment scales and gait parameters from a 10-m walk test. In addition to gait parameters, we also set up a modified gait symmetric index to infer functional recovery results, the Fugl-Meyer assessment scale, and the Berg balance scale. These results explore only the average walking speeds correlate moderately with these two scales (γ = 0.62, 0.68, n = 23), while the modified gait symmetry index owns the moderate negative correlation(γ =-0.51, -0.52, n=23).
Conclusively, with appropriate algorithms and suitable locations to deploy inertial sensors, inertial-based wearable devices demonstrate promising applications in classifying neuromotor and functional recovery among patients after stroke. The modified gait symmetry index warrants further exploring the possibility of inferring functional recovery. | en_US |