The paper introduces a training system for foot ulcer patients based on three axis accelerometer, which uses three axis accelerometer and Apple mobile phone platform to guide foot ulcer patients to carry out a variety of lower limb muscle tissues training. The acceleration values of three directions for the foot training is obtained by analog-to-digital conversion and transmitted to the Apple mobile phone via its Bluetooth low energy. The Apple mobile phone accomplishes acceleration data preprocessing, numerical filtering and adaptive dual-threshold processing by our developed application program, so as to achieve the purpose of foot gesture recognition. The experimental result shows that the design can effectively present the training situation and effect of patients, encourage patients to adhere to the training, and provide some reference data for doctors and patients.
In order to accurately evaluate the similarity of motions during daily rehabilitation training for stroke patients, this paper proposed a novel quantitative assessment method based on dynamic time warping (DTW) algorithm. Firstly, the raw accelerometer signals were preprocessed to eliminate the noise. Secondly, the similarity between the accelerometer signals and four standard task templates was calculated respectively, and then the motion was recognized based on the similarity measurements. Finally, the corresponding quantitative assessment model was used to compute the result. The clinical experimental results showed that there were significant differences in the shortest path distance (R value) of DTW between different tasks, and the classification accuracy could be up to 91% when the R value was selected as the classification feature. Additionally, with the process of rehabilitation, the R value decreased gradually, which means that the R value can be taken as the assessment index to evaluate the quality of designated tasks for stroke patients. It also indicated that the R value could be applied into the scene of automatic prescription generation and interactive gaming to determine whether it is needed to change the rehabilitation plan or adjust the game difficulty level, so as to implement the individualized rehabilitation services.
Ballistocardiogram (BCG) and electrocardiogram (ECG) can realize the detection of cardiac function from mechanical and electrical dimensions respectively. By extracting the corresponding characteristic parameters of the two signals and carrying out joint analysis, an important cardiac physiological index such as cardiac contractility, can be reflected. To overcome the shortcomings of complication and heaviness of the existing acquisition equipment, a wearable BCG-ECG signal acquisition system is designed in this paper, which realizes BCG signal acquisition based on accelerometer and ECG signal acquisition based on conductive rubber electrodes. The signals of 6 healthy persons were collected, and BCG signals collected by piezoelectric films were used as reference signals. The waveform characteristics of signals were compared, and the difference of cardiac cycle acquisition was analyzed. The waveform characteristics of the two signals acquired by the device were consistent with the standard signals, and there was no significant difference in the acquisition of the cardiac cycle between the proposed method and the traditional method. The results show that the system can accurately collect human BCG signals and ECG signals. The system provides a basis for subsequent research on BCG signal formation mechanism and health applications.