Recognition of elementary upper limb movements in nomadic environment

Abstract

ICT enabled body-worn remote rehabilitation system has been projected as an effective means for combating the major socio-economic challenge resulting from the need for quality care delivery for stroke survivors. The two major problems faced in such systems are: 1) while effective for characterising the patient’s performance during a constrained “exercise phase” in remote settings, the more natural indicator of rehabilitation status, i.e., the patient’s performance in an “unconstrained nomadic environment”, are often not considered and; 2) being body-worn and thus constrained by the battery life, their sustainability for long-term continuous monitoring is questionable. These shortcomings motivated the: 1) exploration of effective algorithmic strategies for accurately detecting movement of affected body parts, more specifically, the movement of the upper limb since it frequently gets affected by stroke episodes – in unconstrained scenarios and; 2) translation of the algorithms to dedicated low-power hardware with an aim of enhancing the battery life of a resource constrained body-worn sensor based remote rehabilitation system for its sustained operation satisfying the notion of long-term continuous monitoring.

Following instructions of expert physiotherapists, this work concentrates on detecting three fundamental upper limb movements in unconstrained scenarios: extension/flexion of the forearm; rotation of the forearm about the elbow; and rotation of the arm about the long axis of forearm, using body-worn inertial sensors. After selecting the appropriate type of inertial sensors and their positions through exhaustive experiments, two novel algorithms were proposed to recognize the above mentioned movements: 1) clustering and minimum distance classifier based approach and 2) tracking the orientation of an inertial sensor placed on the wrist. The performances of the algorithms have been evaluated prospectively through an archetypal activity ‘making-a-cup-of-tea’ which includes multiple occurrences of the chosen movements. The proposed clustering based approach detected the three movements with an average accuracy of 88% and 70% using accelerometer data and 83% and 70% using gyroscope data obtained from the wrist for healthy subjects and stroke survivors respectively. Compared to that the proposed sensor orientation based methodology using a wrist-worn accelerometer only recognized the three movements with accuracies in the range of 91-99% for healthy subjects and 70%-85% for stroke survivors. However the clustering based approach provides greater flexibility in terms of incorporating new types of movements apart from the ones chosen here and can also be used to track changes in motor functionality over time. Subsequently it was translated into a novel ASIC resulting in dynamic power consumption of 25.9 mW @20 MHz in 130 nm technology. On the other hand, the sensor orientation based approach was also validated in hardware using an Altera DEII FPGA system, for high speed real-time movement recognition.

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