Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information.

Brunnstrom stage is widely used to evaluate the movement function of stroke patients during rehabilitation by physicians. In this paper, a new method, which is based on extreme learning machine (ELM) and the Internet technology, is proposed to realize intelligent Brunnstrom stages evaluation for upper limb movement function of stroke patients. Preliminary experiment has been conducted with movement data collected from 23 stroke patients and 4 healthy people. The experiment results show that, compared with the experienced physicians evaluation results, the accuracy of the established ELM model can reach 92.1%, which means the proposed method is helpful for physicians to remotely evaluate those stroke patients who finish rehabilitation exercises at home or community, and is helpful to solving the problem of the lack of medical resource and the high cost of inpatient rehabilitation.

In order to monitor the rehabilitation training of stroke patients in unsupervised situation and provide rehabilitation advice for rehabilitation clinicians, a wireless upper limb motion recognition system has been developed using tilt sensors, to identify the complex upper limb movements such as flexion and extension of elbow, flexion of elbow and touch the head, from a stroke patient's rehabilitation program. 18 different movements from a stroke patient's rehabilitation training program were adopted to verify and validate this system with 12 of them in the training group and 6 of them in the testing group. After preprocessing and the feature extraction of the acquired motion data, the Support Vector Machine (SVM) recognition approach was employed to establish a small sample identification model. Finally, the data of testing group in the upper limb rehabilitation training program were used to identify the developed model. It has been found that the recognition accuracy from this developed model was 100%. This result provides a well reference for further development of an automated system for stroke patient rehabilitation motion recognition.