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Rehabilitation procedure and performance measurement using mechanical rotary impedance actuator
Widyotriatmo A.a, Maharnani C.a, Suprijantoa
a Instrumentation and Control, Faculty of lndustrial Technology, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 [International Journal of Artificial Intelligence].This paper proposes a procedure and metrics for performance assessment in limb rehabilitation using a mechanical rotary impedance actuator. One joint mechanical rotary actuator with a force sensor at the end effector is used as a prototype for rehabilitation of flexion movement of the arm. A trajectory for rehabilitation is developed consisting of two parameters which are the time of completion and the achievable range of motion (ROM). An impedance control is developed using free-regressor adaptive control, producing interaction force between the actuator and the patient. Based on patient’s condition, the clinician can adjust the impedance setting of the rotary joint so that the arm rehabilitation actuator assists the patient only as much as needed. By setting the parameters of impedance and the rehabilitation trajectory, the patient follows a training procedure as in the active mode in which the impedance of the rotary joint is adjustable. In the proposed procedure, the patient is asked to follow the generated rehabilitation trajectory. The metrics are developed containing the motor function which is how close the patient can follow the rehabilitation trajectory, the maximum ROM a patient can achieve, and the strength index which is the maximum force that a patient can provide. Those metrics are calculated as the training procedure goes on. Ten healthy subjects perform the training procedure and the metrics show consistent results. Accordingly, the proposed metrics are potentially able to classify the ability of the patients and therefore can be used for monitoring the progress of a patient during the rehabilitation.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Control,Nonlinear compensation,Proportional-integral,Rehabilitation robot,Trajectory generation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]