Delay reduction in real time recognition of human activity for stroke rehabilitation
Assisting patients to perform activity of daily living (ADLs) is a challenging task for both human and machine. Hence, developing a computer based rehabilitation system to re train patients to carry out daily activities is an essential step towards facilitating rehabilitation of stroke patients with apraxia and action disorganization syndrome (AADS) This paper presents a real time hidden Markov model (HMM) based human activity recognizer, and proposes a technique to reduce the time delay occurred during the decoding stage. Results are reported for complete tea making trials. In this study, the input features are recorded using sensors attached to the objects involved in the tea making task, plus hand coordinate data captured using KinectTM sensor. A coaster of sensors, comprising an accelerometer and three force sensitive resistors, are packaged in a unit which can be easily attached to the base of an object. A parallel asynchronous set of detectors, each responsible for the detection of one sub goal in the tea making task, are used to address challenges arising from overlaps between human actions. The proposed activity recognition system with the modified HMM topology provides a practical solution to the action recognition problem and reduces the time delay by 64% with no loss in accuracy.
2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE)