1. Introduction
As of 2016, Japan’s population of elderly people older than 65 years old was 34.61 million, accounting for 27.3 percent of the whole population [
1]. This figure underscores Japan’s rapidly aging society. According to Ministry of Health, Labor and Welfare statistics, the primary reason for care nursing is a stroke, accounting for 21.5% [2]. Cerebral stroke causes intellectual disability and language disorders, together with hemiplegia of the legs. Early rehabilitation is necessary to treat hemiplegia. In fact, training to improve joint range motion and muscle strength is often conducted using a continuous passive motion (CPM) device [3] [4]. However, such efforts are ineffective after the chronic phase: after more than six months have passed after the cerebral infarction. Recently, so-called “repetitive facilitative exercise” (Kawahira method) [5] [6] has drawn attention. Stimulation is given to the neural system (brain) by watching an affected limb with motor intention (imagery). Simultaneously, the therapist moves the limb. Also, stimulation is applied to injured muscles or tendons. Such treatment might facilitate development of a novel neural circuit responsible for intentional movement by reconstructing novel circuitry and achieving motor ability improvement. Known as neurorehabilitation, this process is expected to be applicable and beneficial for chronic patients. Here, rehabilitation in relation to ankle is of our interest, and several studies have been addressing the issue of ankle control and improvement [4][7]. Among promising techniques is the brain–computer interface (BCI) that operates the rehabilitation device. Such a device provides visual stimulation that evokes brain activity of event-related potentials (ERP) [8] [9] or steady state visual evoked potentials (SSVEP) that correspond to the stimulus temporal frequency [10] [11]. Nevertheless, it is difficult to include intention or imagery such as direction or velocity of body movement. Only temporal information (timing) is a concern. Another example is to use frequency information to use synchronous and non-synchronous components of brain waves relating to a particular event (event related synchronization/desynchronization, ERS/ERD) [12] [13] [14].
In addition, there is a big problem that it is difficult to perform the rehabilitation in the same day because all these methods take the feature extraction from several weeks to several months by machine learning.
The current study was undertaken to construct a novel neurorehabilitation system to extract motor intention more easily and quickly by specifically emphasizing the amplitude increase/decrease using Fuzzy Template Matching (FTM). Consequently, we are free to assess frequency and oscillatory factors such as ERS/ERD.
This report describes the development of such a novel rehabilitation system for lower limb movement, including a method of learning FTM, the device mechanism, the control design, and the system setup. Actual human brain activity can be measured as a consequence of the operation and verification of the rehabilitation system.