This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

Physical fitness is the overall ability of the body to adapt to the working environment and perform sporting and daily activities. The aim of this study was to analyze the correlation between muscle fitness and the electromyography (EMG) signals of lower limbs under varying exercise intensity. The standing long jump was used as a test task for assessing the power of the lower limb muscles. Participants were university freshmen who belonged to the top 20%, middle 20%, and bottom 20% groups in terms of physical fitness norms. The EMG signals of the participants’ lower limbs while they performed squats were collected under four exercise intensities of repetitions maximum (RM): no load, 8RM, 18RM, and 28RM; the features of the signals were extracted using time-domain and frequency-domain analysis. Statistical analysis was also performed. The top and bottom groups exhibited significant differences time-domain indicators mean absolute value (MAV) and average amplitude change (AAC) in the low-intensity exercise (28RM). The MAV, variance of EMG (VAR), root mean square (RMS), and AAC were significantly different between the top and bottom groups in the three graded intensities (8RM, 18RM, and 28RM). The mean frequency (MNF) and median frequency (MDF), which are frequency-domain indicators, were significantly different between the top and bottom groups in the low-intensity (28RM) and moderate-intensity (18RM) exercises.

Postural orthostatic tachycardia syndrome (POTS) typically occurs in youths, and early accurate POTS diagnosis is challenging. A recent hypothesis suggests that upright cognitive impairment in POTS occurs because reduced cerebral blood flow velocity (CBFV) and cerebrovascular response to carbon dioxide (CO₂) are nonlinear during transient changes in end-tidal CO₂ (PETCO₂). This novel study aimed to reveal the interaction between cerebral autoregulation and ventilatory control in POTS patients by using tilt table and hyperventilation to alter the CO₂ tension between 10 and 30 mmHg. The cerebral blood flow velocity (CBFV), partial pressure of end-tidal carbon dioxide (PETCO₂), and other cardiopulmonary signals were recorded for POTS patients and two healthy groups including those aged >45 years (Healthy-Elder) and aged <45 years (Healthy-Youth) throughout the experiment. Two nonlinear regression functions, Models I and II, were applied to evaluate their CBFV-PETCO₂ relationship and cerebral vasomotor reactivity (CVMR). Among the estimated parameters, the curve-fitting Model I for CBFV and CVMR responses to CO₂ for POTS patients demonstrated an observable dissimilarity in CBFV_max (p = 0.011), mid-PETCO₂ (p = 0.013), and PETCO₂ range (p = 0.023) compared with those of Healthy-Youth and in CBFV_max (p = 0.015) and CVMR_max compared with those of Healthy-Elder. With curve-fitting Model II for POTS patients, the fit parameters of curvilinear (p = 0.036) and PETCO₂ level (p = 0.033) displayed significant difference in comparison with Healthy-Youth parameters; range of change (p = 0.042), PETCO₂ level, and CBFV_max also displayed a significant difference in comparison with Healthy-Elder parameters. The results of this study contribute toward developing an early accurate diagnosis of impaired CBFV responses to CO₂ for POTS patients.

Current paper focus on Parkinson's patients with autonomic dysfunction and how their interactions between cerebral autoregulation and ventilatory control are affected. The experimental data of dynamic CA assessment from the ANS Laboratory of CCGH was

Muscular fitness is not only the ability of the body to adapt to work and the environment but also the operational ability of physical behavior. We speculated whether research could be conducted on the theory of muscular fitness and its qualitative/quantitative relationship based on muscular fitness and exercise physiology from the perspective of muscular endurance and muscular exploration. This study used standing long jumps as a standard metric for physical fitness to identify the bottom 20% groups. The experiment involved eight freshmen from the bottom 20% groups, and the pre-tests of the participants’ electromyography (EMG) signals under different exercise intensities were measured and after performing a set of intensive exercises for post-tests. The signal’ characteristics measured in time and frequency domains were analyzed to find the correlation between them and the participants’ muscular fitness. Weighted squats were chosen as the strength movements, which were separated into an exercise experiment and a force plate experiment. Both experiments included three different exercise intensities: 8 repetition maximum (RM), 18RM, and 28RM. The EMG signals were captured and analyzed in both time and the frequency domains. Finally, paired sample tests were performed to determine the difference of features under different exercise intensities. The comparison of readings before and after intensive exercises shows that, for the exercised experiment, a significant difference in the mean absolute (MAV), the variance of EMG (VAR), the root mean square value (RMS), and the average amplitude of change (AAC) was observed under 8RM. Under 18RM, MAV, VAR, and AAC showed a significant difference. In the force plate experiment, RMS, AAC, mean frequency (MNF), and median frequency (MDF) showed a statistically significant difference under the intensity of 18RM. As for intensity under 28RM, MAV, VAR, RMS, and AAC also showed significant difference.