# A Brunnstrom Stage Evaluation System with Fuzzy-Accelerometer
## 1 Introduction  
In recent years, the World Health Organization (WHO) has repeatedly issued a \"1 in 6\" warning referring to the likelihood that 1 in 6 people can expect to suffer a stroke sometime during their lifetime, regardless of age, gender or ethnicity [1]. Stroke incidents not only entail high medical costs for acute treatment proximate to the stroke incident, but also require intensive, long-term physical rehabilitation to restore mobility, resulting in huge costs to families and society at large. Limited human resources create an important need for, information technology and sensing devices that can be used to monitor and evaluate the rehabilitation status of hemiplegia patients. During WWII, [PERSON] noted that hemiplegia patients seem to follow a stereotyped recovery process. Between 1954 and 1956, observation of 100 hemiplegia patients was used to divide the recovery process into seven distinct periods, referred to as Brunnstrom Stages [2], but a clear evaluation model was lacking. Later, in 1974, [PERSON]. [PERSON] established a clear stage evaluation model based on [PERSON]'s staging test, and made suggestions for appropriate treatment, and these methods are still used by clinicians today as the basis for diagnosis of degree of hemiplegia [3]. To develop an appropriate rehabilitation strategy, physicians need to first evaluate the current degree of hemiplegia, using methods including the Brunnstrom Approach, the Fugl-Meyer Assessment (FMA) [4] and the Ueda 12-Stage Hemiplegic Function Test [5]. Human body posture recognition is often used in rehabilitation evaluation, sports and film production. The main methods include the use of an Oqus camera, Kinect sensors, and Inertial Measurement Units (IMU). An optical high-speed camera is used to record the movement of reflective markers attached at specific points on the surface of the human body, creating a series of dot trajectorydata which describe the movement of each limb. This approach is often used to produce animation or to analyze the movement of athletes [6]. While this measurement method can accurately describe human body movement, it requires data collection equipment to be fixed in space along with significant high-speed computing resources for movement analysis. Somatosensory detectors use an infrared depth camera to capture skeletal images for motion capture and analysis [7; 8]. This method does not require the subject to wear sensors, and thus does not impede subject movement. However this approach is relatively expensive, and is subject to limitations imposed by the recording venue and the number of subjects, making it inapplicable to the current case. With the rapid development of sensing technologies in recent years, inertial sensors have gradually emerged as the mainstream method for capturing motion signals, including accelerometers, gyroscopes, and magnetic sensors. Of these, accelerometers provide good performance with low power consumption and low cost [9]. In addition, the development of Micro Electro Mechanical Systems (MEMS) allows for such sensors to be worn directly in various places on the human body, with data collected, processed and transmitted in real time anywhere and anytime. Acceleration sensors are now widely used in various types of human motion recognition research, including common daily motion activities such as walking [10], running [11], and ascending/descending stairs [12]. Real-time human motion recognition is useful for fall detection in home care research [13]. Machine learning techniques can be applied to longitudinal recording of human motion patterns to allow for more precise classification and recognition of various movement types [14]. Some studies have focused on the evaluation of hemiplegia patients [15; 16] using machine learning techniques to train samples for the development of classification and evaluation models. While such approaches can provide highly accurate identification results, they require very large learning samples and complex mathematical operations.