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.
|Title of host publication||Proceedings of 2016 first International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||5|
|Publication status||Published - 4 Aug 2016|
- Hidden Markov Models