With conventional stethoscopes, the auscultation results may vary from one doctor to another due to a decline in his/her hearing ability with age or his/her different professional training, and the problematic cardiopulmonary sound cannot be recorded for analysis. In this paper, to resolve the above-mentioned issues, an electronic stethoscope was developed consisting of a traditional stethoscope with a condenser microphone embedded in the head to collect cardiopulmonary sounds and an AI-based classifier for cardiopulmonary sounds was proposed. Different deployments of the microphone in the stethoscope head with amplification and filter circuits were explored and analyzed using fast Fourier transform (FFT) to evaluate the effects of noise reduction. After testing, the microphone placed in the stethoscope head surrounded by cork is found to have better noise reduction. For classifying normal (healthy) and abnormal (pathological) cardiopulmonary sounds, each sample of cardiopulmonary sound is first segmented into several small frames and then a principal component analysis is performed on each small frame. The difference signal is obtained by subtracting PCA from the original signal. MFCC (Mel-frequency cepstral coefficients) and statistics are used for feature extraction based on the difference signal, and ensemble learning is used as the classifier. The final results are determined by voting based on the classification results of each small frame. After the testing, two distinct classifiers, one for heart sounds and one for lung sounds, are proposed. The best voting for heart sounds falls at 5–45% and the best voting for lung sounds falls at 5–65%. The best accuracy of 86.9%, sensitivity of 81.9%, specificity of 91.8%, and F1 score of 86.1% are obtained for heart sounds using 2 s frame segmentation with a 20% overlap, whereas the best accuracy of 73.3%, sensitivity of 66.7%, specificity of 80%, and F1 score of 71.5% are yielded for lung sounds using 5 s frame segmentation with a 50% overlap.

This paper presents a healthcare information and medical resource management platform utilizing wearable devices, physiological sensors, and an indoor positioning system (IPS). This platform provides medical healthcare information management based on the physiological information collected by wearable devices and Bluetooth data collectors. The Internet of Things (IoT) is constructed for this medical care purpose. The collected data are classified and used to monitor the status of patients in real time with a Secure MQTT mechanism. The measured physiological signals are also used for developing an IPS. When the patient is out of the safety zone, the IPS will send an alert message instantly by pushing the server to remind the caretaker, easing the caretaker’s burden and offering extra protection for the patient. The presented system also provides medical resource management with the help of IPS. The medical equipment and devices can be tracked by IPS to tackle some equipment rental problems, such as lost and found. A platform for the medical staff work coordination information exchange and transmission is also developed to expedite the maintenance of medical equipment, providing the shared medical information to healthcare and management staff in a timely and transparent manner. The presented system in this paper will finally reduce the loading of medical staff during the COVID-19 pandemic period.

Thanks to the rapid development of Wearable Fitness Trackers (WFTs) and Smartphone Pedometer Apps (SPAs), people are keeping an eye on their health through fitness and heart rate tracking; therefore, home weight training exercises have received a lot of attention lately. A multi-procedure intelligent algorithm for weight training using two inertial measurement units (IMUs) is proposed in this paper. The first procedure is for motion tracking that estimates the arm orientation and calculates the positions of the wrist and elbow. The second procedure is for posture recognition based on deep learning, which identifies the type of exercise posture. The final procedure is for exercise prescription variables, which first infers the user’s exercise state based on the results of the previous two procedures, triggers the corresponding event, and calculates the key indicators of the weight training exercise (exercise prescription variables), including exercise items, repetitions, sets, training capacity, workout capacity, training period, explosive power, etc.). This study integrates the hardware and software as a complete system. The developed smartphone App is able to receive heart rate data, to analyze the user’s exercise state, and to calculate the exercise prescription variables automatically in real-time. The dashboard in the user interface of the smartphone App can display exercise information through Unity’s Animation System (avatar) and graphics, and records are stored by the SQLite database. The designed system was proven by two types of experimental verification tests. The first type is to control a stepper motor to rotate the designed IMU and to compare the rotation angle obtained from the IMU with the rotation angle of the controlled stepper motor. The average mean absolute error of estimation for 31 repeated experiments is 1.485 degrees. The second type is to use Mediapipe Pose to calculate the position of the wrist and the angles of upper arm and forearm between the Z-axis, and these calculated data are compared with the designed system. The root-mean-square (RMS) error of positions of the wrist is 2.43 cm, and the RMS errors of two angles are 5.654 and 4.385 degrees for upper arm and forearm, respectively. For posture recognition, 12 participants were divided into training group and test group. Eighty percent and 20% of 24,963 samples of 10 participants were used for the training and validation of the LSTM model, respectively. Three-thousand-three-hundred-and-fifty-nine samples of two participants were used to evaluate the performance of the trained LSTM model. The accuracy reached 99%, and F1 score was 0.99. When compared with the other LSTM-based variants, the accuracy of one-layer LSTM presented in this paper is still promising. The exercise prescription variables provided by the presented system are helpful for weight trainers/trainees to closely keep an eye on their fitness progress and for improving their health.

Lower extremity exercises are considered a standard and necessary treatment for rehabilitation and a well-rounded fitness routine, which builds strength, flexibility, and balance. The efficacy of rehabilitation programs hinges on meticulous monitoring of both adherence to home exercise routines and the quality of performance. However, in a home environment, patients often tend to inaccurately report the number of exercises performed and overlook the correctness of their rehabilitation motions, lacking quantifiable and systematic standards, thus impeding the recovery process. To address these challenges, there is a crucial need for a lightweight, unbiased, cost-effective, and objective wearable motion capture (Mocap) system designed for monitoring and evaluating home-based rehabilitation/fitness programs. This paper focuses on the development of such a system to gather exercise data into usable metrics. Five radio frequency (RF) inertial measurement unit (IMU) devices (RF-IMUs) were developed and strategically placed on calves, thighs, and abdomens. A two-layer long short-term memory (LSTM) model was used for fitness activity recognition (FAR) with an average accuracy of 97.4%. An intelligent smartphone algorithm was developed to track motion, recognize activity, and calculate key exercise variables in real time for squat, high knees, and lunge exercises. Additionally, a 3D avatar on the smartphone App allows users to observe and track their progress in real time or by replaying their exercise motions. A dynamic time warping (DTW) algorithm was also integrated into the system for scoring the similarity in two motions. The system’s adaptability shows promise for applications in medical rehabilitation and sports.

Stroke is the fifth leading cause of death in Taiwan. In the process of stroke treatment, rehabilitation for gait recovery is one of the most critical aspects of treatment. The Gait Assessment and Intervention Tool (G.A.I.T.) is currently used in clinical practice to assess the gait recovery level; however, G.A.I.T. heavily depends on physician training and clinical judgment. With the advancement of technology, today’s small, lightweight inertial measurement unit (IMU) wearable sensors are rapidly revolutionizing gait assessment and may be incorporated into routine clinical practice. In this paper, we developed a gait data acquisition and analysis system based on IMU wearable devices, proposed a simple yet accurate calibration process to reduce the IMU drifting errors, designed a machine learning algorithm to obtain real-time coordinates from IMU data, computed gait parameters, and derived a formula for G.A.I.T. scores with significant correlation with the physician’s observational scores.

This research focuses on developing a low-cost automated demand response controller (DRC) with OpenADR 2.0a capability to enable existing infrared-controlled (IR-controlled) air conditioners (ACs) in homes and buildings to participate in automated demand response programs (ADRPs). The DRC consists of four modules: a smart socket module, an infrared module, a temperature sensor, and a voltage/current module. It can receive, analyze, and respond to demand response (DR) events and perform necessary demand and energy control strategies via IR. Power line communication (PLC) is used for communication without additional wiring. The system is tested under two conditions: participating in ADRPs via OpenADR and not participating in ADRPs. An 8.8% load reduction is observed with different temperature settings when not participating in ADRPs, and energy reductions of 21% to 46% are achieved using various cooling/fanning duty cycles in ADRPs. The proposed system can be integrated with any DR algorithm to meet demand management requirements under the OpenADR program, contributing to significant demand reductions.