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Rehabilitation is a necessary restoration process of recovering impaired joint motion and muscle strength. Recent trends of rehabilitation have also moved towards providing more participation of the patient in therapy rather than simple passive treatments as it has been demonstrated to be non-trivial in promoting neural plasticity meant to promote motor recovery process. This paper presents an assistive control strategy based on impedance control technique. Dynamic modelling of upper arm is obtained by utilising the Euler-Lagrange formulation. The proportional-derivative (PD), computed torque control (CTC) impedance based framework is applied to examine its effectiveness in performing joint-space control with objectives specified in rehabilitating the elbow joint along the sagittal plane. A feasibility study through simulation was carried out to investigate the efficacy of the proposed controller on acceleration-based impedance model. The results show that impedance controller is more suitable as it allows the cooperative effort of the patient.

Spasticity is common symptom presented amongst people with sensorimotor disabilities. Imbalanced signals from the central nervous systems (CNS) which are composed of the brain and spinal cord to the muscles ultimately leading to the injury and death of motor neurons. In clinical practice, the therapist assesses muscle spasticity using a standard assessment tool like Modified Ashworth Scale (MAS), Modified Tardiue Scale (MTS) or Fugl-Meyer Assessment (FMA). This is done subjectively based on the experience and perception of the therapist subjected to the patient fatigue level and body posture. However, the inconsistency in the assessment is prevalent and could affect the efficacy of the rehabilitation process. Thus, the aim of this paper is to describe the methodology of data collection and the quantitative model of MAS developed to satisfy its description. Two subjects with MAS of 2 and 3 spasticity levels were involved in the clinical data measurement. Their level of spasticity was verified by expert therapist using current practice. Data collection was established using mechanical system equipped with data acquisition system and LABVIEW software. The procedure engaged repeated series of flexion of the affected arm that was moved against the platform using a lever mechanism and performed by the therapist. The data was then analyzed to investigate the characteristics of spasticity signal in correspondence to the MAS description. Experimental results revealed that the methodology used to quantify spasticity satisfied the MAS tool requirement according to the description. Therefore, the result is crucial and useful towards the development of formal spasticity quantification model.