Measuring human trainers' skill for the design of better robot control algorithms for gait training after spinal cord injury

Abstract

This paper presents work towards quantifying the manual assistance provided by therapists during locomotor training for people with spinal cord injury. The final goal is to translate human trainers' skill into gait-training robot algorithms. Locomotor training is a rehabilitation technique in which three therapists assist the legs and hip of the patient to walk on a treadmill while part of the patient's body weight is supported by an overhead harness. We have developed a sensorized orthosis that measures shank kinematics and therapist forces during locomotor training. The orthosis is attached to one of the legs, so that one of the therapists assists through the orthotic interface. This interface is similar to how a locomotor-training robot is attached to the patient's shank. However, the force and intelligence behind the orthosis is not robotic, but human. Our intention is to quantify and analyze the human therapists' intelligence and expertise to help design better gait-training robot control algorithms. In this paper we present some preliminary results from the first locomotor training sessions with spinal cord injured patients using this sensor system. A key initial finding is that even skilled trainers assist with substantial differences in terms of both forces and motions. With the same patient, same stepping speed and same body weight support, the differences in peak forces applied to the knee between trainers were up to 100% in some sessions.