Personalized Rehabilitation Recognition Model upon ANFIS

This study applied the Adaptive Neuro-Fuzzy Inference System (ANFIS) to design a recognition model of personalized rehabilitation. In the model, the user may take a wearable sensor and follow the assigned joint-relax exercise to measure the motions of the upper limbs. The sensor that is embedded with the chips of accelerometer, gyroscope, and inclinometer produced the sample datasets due to the exercise schedule of physiotherapy assignment. All motion samples were labeled by arbitrary numbers, which can be identified to the specific motion, for the data training process. A Fuzzy Inference System (FIS) was initially designed by the steps of data pre-processing, featuring, fuzzifying, and ruling Fuzzy logics according to the sample datasets. The FIS was then trained by the ANFIS for optimization by tuning parameters of the features. In testing, the accomplished FIS could recognize the motion features by the defuzzifier that infers the label corresponding to the motion. As a result, the average recognition rate was higher than 90% when the testing motions followed the sampling schedule of the physiotherapy assignment. The model can be applied in the ubiquitous healthcare measurement for health services. The professionals can assess whether the subject obeyed the assigned program or not based on detail motions of the exercise. This approach can be enabled on the trackable interface for the physiatrists to screen the motions of routine rehabilitation

Altered Activation of Innate Immunity Associates with White Matter Volume and Diffusion in First-Episode Psychosis

First-episode psychosis (FEP) is associated with inflammatory and brain structural changes, but few studies have investigated whether systemic inflammation associates with brain structural changes in FEP. Thirty-seven FEP patients (median 27 days on antipsychotic medication), and 19 matched controls were recruited. Serum levels of 38 chemokines and cytokines, and cardiovascular risk markers were measured at baseline and 2 months later. We collected T1-and diffusion-weighted MRIs with a 3 T scanner from the patients at baseline. We analyzed the association of psychosis-related inflammatory markers with gray and white matter (WM) volume using voxel-based morphometry and WM diffusion using tract-based spatial statistics with whole-brain and region-of-interest (ROI) analyses. FEP patients had higher CCL22 and lower TGFa, CXCL1, CCL7, IFN-alpha 2 and ApoA-I than controls. CCL22 decreased significantly between baseline and 2 months in patients but was still higher than in controls. The association between inflammatory markers and FEP remained significant after adjusting for age, sex, smoking and BMI. We did not observe a correlation of inflammatory markers with any symptoms or duration of antipsychotic treatment. Baseline CCL22 levels correlated negatively with WM volume and positively with mean diffusivity and radial diffusivity bilaterally in the frontal lobes in ROI analyses. Decreased serum lan association between circulating chemokine levels and WM in FEP patients. Interestingly, CCL22 has been previously implicated in autoimmune diseases associated with WM pathology. The results suggest that an altered activation of innate immunity may contribute to WM damage in psychotic disorders.evel of ApoA-I was associated with smaller volume of the medial temporal WM. In whole-brain analyses, CCL22 correlated positively with mean diffusivity and radial diffusivity, and CXCL1 associated negatively with fractional anisotropy and positively with mean diffusivity and radial diffusivity in several brain regions. This is the first report to demonstratePeer reviewe

An Activity Recognition Model Using Inertial Sensor Nodes in a Wireless Sensor Network for Frozen Shoulder Rehabilitation Exercises

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

Personalized Rehabilitation Recognition for Ubiquitous Healthcare Measurements

The physical therapeutic application needs personalized rehabilitation recognition (PRR) for ubiquitous healthcare measurements (UHMs). This study employed the adaptive neuro-fuzzy inference system (ANFIS) to generate a PRR model for a self-development system of UHM. The subjects wore a sensor-enabled wristband during physiotherapy exercises to measure the scheduled motions of their limbs. In the model, the sampling data collected from the scheduled motions are labeled by an arbitrary number within a defined range. The sample datasets are referred as the design of an initial fuzzy inference system (FIS) with data preprocessing, feature visualizing, fuzzification, and fuzzy logic rules. The ANFIS then processes data training to adjust the FIS for optimization. The trained FIS then can infer the motion labels via defuzzification to recognize the features in the test data. The average recognition rate was higher than 90% for the testing motions if the subject followed the sampling schedule. With model implementation, the middle section of motion datasets in each second is recommended for recognition in the UHM system which also includes a mobile App to retrieve the personalized FIS in order to trace the exercise. This approach contributes a PRR model with trackable diagrams for the physicians to explore the rehabilitation motions in details

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

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

Risk of Venous Thromboembolic Events in Patients with Osteonecrosis of the Femoral Head Undergoing Primary Hip Arthroplasty

Previous data have shown patients with osteonecrosis of the femoral head (ONFH) have increased lifelong risk of unprovoked venous thromboembolic events (VTE) as compared with the general population, according to sharing common pathological mechanism of endothelial dysfunction. However, whether the risk of VTE increases in those ONFH patients undergoing major hip replacement surgery remains unclear. This is a retrospective nationwide Asian population-based study. From 1997 to 2013, a total of 12,232 ONFH patients receiving partial or total hip replacement for the first time and revision surgeries were retrospectively selected from Taiwan Health Insurance surgical files. By 1:1 matching on age, sex, surgical types, and socioeconomic status, 12,232 subjects without ONFH undergoing similar hip surgery were selected as non-ONFH group. The incidence and risk of post-surgery VTE, including deep venous thrombosis (DVT) and pulmonary embolism (PE), were compared between the ONFH and non-ONFH groups. Results showed that 53.8% of ONFH patients were male and the median age was 61.9 years old. During the mean follow-up period of 6.4 years, the incidences of VTE (1.4% vs. 1.2%), DVT (1.1% vs. 0.9%), and PE (0.4% vs. 0.4%) were slightly but insignificantly higher in the ONFH than in the non-ONFH group undergoing the same types of major hip replacement surgery (all p-values > 0.250). Concordantly, we found no evidence that the risk of VTE was increased in the ONFH patients as compared with the non-ONFH counterparts (adjusted HR 1.14; 95% CI 0.91–1.42; p = 0.262). There were also no increased risks for DVT and PE in the ONFH subgroups stratified by comorbidities, drug exposure to pain-killer or steroid, and follow-up duration after surgery, either. In conclusion, hip arthroplasty in Asian patients with ONFH is associated with similar rates of VTE as compared to patients with non-ONFH diagnoses