A wearable wireless inertial sensor node for body motion analysis is designed and implemented. A triaxial accelerometer, a biaxial gyroscope, and a yaw rate gyroscope are employed to sense the accelerations and angular rates of the object attached. These three ICs, a wireless mote, the power circuit, and a modified printed inverted-F antenna are integrated on a four-layer printed circuit board. To achieve the compact size for comfortable wearing, the printed antenna is deliberately designed as small as possible, while maintaining a reasonable antenna performance. The proposed inertial sensor node is also easily adaptable to applications with different power requirements because of the consideration of the periodic and moving-event wakeup in the software design. The static characteristics and Allan deviations of the implemented node are measured and analyzed. The false alarm rate of a moving detection based on Bayes rule is presented and a threshold is suggested. Finally, the raw data of several body motions are measured and those behaviors are observed apparently. The proposed inertial sensor node is compact and easily wearable; hence, it is feasible to be applied to the body motion analysis.

This paper proposes a model for recognizing motions performed during rehabilitation exercises for frozen shoulder conditions. The model consists of wearable wireless sensor network (WSN) inertial sensor nodes, which were developed for this study, and enables the ubiquitous measurement of bodily motions. The model employs the back propagation neural network (BPNN) algorithm to compute motion data that are formed in the WSN packets; herein, six types of rehabilitation exercises were recognized. The packets sent by each node are converted into six components of acceleration and angular velocity according to three axes. Motor features such as basic acceleration, angular velocity, and derivative tilt angle were input into the training procedure of the BPNN algorithm. In measurements of thirteen volunteers, the accelerations and included angles of nodes were adopted from possible features to demonstrate the procedure. Five exercises involving simple swinging and stretching movements were recognized with an accuracy of 85%–95%; however, the accuracy with which exercises entailing spiral rotations were recognized approximately 60%. Thus, a characteristic space and enveloped spectrum improving derivative features were suggested to enable identifying customized parameters. Finally, a real-time monitoring interface was developed for practical implementation. The proposed model can be applied in ubiquitous healthcare self-management to recognize rehabilitation exercises.

This study is aimed to establish a wireless sensor network (WSN) gateway model prior to the back-end server for diverse environmental monitoring applications. The design catalogs different sensor data with transmission load balance to incorporate heterogeneity of sensor signals, stability of data transportation, and expenditure of mobile communication. As considering a variety of sensor characteristics for environmental monitoring, the proposed WSN gateway is designed with three bridged functions, including serial listener, transaction logger and Internet listener, to enable analog and digital signal conversion, physical data classification, threshold determination, database redundancy and mobile communication. The criterion-based scheme is created for remotely updating thresholds of monitored data from the back-end server to the gateway. The model is built upon the embedded computer for low power consuming to perform efficient and stable applications. The comprehensive model design can be easily utilized for general WSN surveillance with expansibility and flexibility.

This paper presents the structure analysis, numerical design simulation, fabrication, and measurements of printed dipole-antennas for wireless communications. Two types of integrated microstrip bal...

Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isolated healthcare in tracking rehabilitation exercises for clinical diagnosis. This study combined a personalized rehabilitation recognition (PRR) system with the AIoT for the UHM of lower-limb rehabilitation exercises. The three-tier infrastructure integrated the recognition pattern bank with the sensor, network, and application layers. The wearable sensor collected and uploaded the rehab data to the network layer for AI-based modeling, including the data preprocessing, featuring, machine learning (ML), and evaluation, to build the recognition pattern. We employed the SVM and ANFIS methods in the ML process to evaluate 63 features in the time and frequency domains for multiclass recognition. The Hilbert-Huang transform (HHT) process was applied to derive the frequency-domain features. As a result, the patterns combining the time- and frequency-domain features, such as relative motion angles in y- and z-axis, and the HHT-based frequency and energy, could achieve successful recognition. Finally, the suggestive patterns stored in the AIoT-PRR system enabled the ML models for intelligent computation. The PRR system can incorporate the proposed modeling with the UHM service to track the rehabilitation program in the future.

Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.