This paper describes an in-vehicle nonintrusive biopotential measurement system for driver health monitoring and fatigue detection. Previous research has found that the physiological signals including eye features, electrocardiography (ECG), electroencephalography (EEG) and their secondary parameters such as heart rate and HR variability are good indicators of health state as well as driver fatigue. A conventional biopotential measurement system requires the electrodes to be in contact with human body. This not only interferes with the driver operation, but also is not feasible for long-term monitoring purpose. The driver assistance system in this paper can remotely detect the biopotential signals with no physical contact with human skin. With delicate sensor and electronic design, ECG, EEG, and eye blinking can be measured. Experiments were conducted on a high fidelity driving simulator to validate the system performance. The system was found to be able to detect the ECG/EEG signals through cloth or hair with no contact with skin. Eye blinking activities can also be detected at a distance of 10 cm. Digital signal processing algorithms were developed to decimate the signal noise and extract the physiological features. The extracted features from the vital signals were further analyzed to assess the potential criterion for alertness and drowsiness determination.

In this paper, we report the design and experimental validation of a novel optical sensor for radial artery pulse measurement based on fiber Bragg grating (FBG) and lever amplification mechanism. Pulse waveform analysis is a diagnostic tool for clinical examination and disease diagnosis. High fidelity radial artery pulse waveform has been investigated in clinical studies for estimating central aortic pressure, which is proved to be predictors of cardiovascular diseases. As a three-dimensional cylinder, the radial artery needs to be examined from different locations to achieve optimal pulse waveform for estimation and diagnosis. The proposed optical sensing system is featured as high sensitivity and immunity to electromagnetic interference for multilocation radial artery pulse waveform measurement. The FBG sensor can achieve the sensitivity of 8.236 nm/N, which is comparable to a commonly used electrical sensor. This FBG-based system can provide high accurate measurement, and the key characteristic parameters can be then extracted from the raw signals for clinical applications. The detecting performance is validated through experiments guided by physicians. In the experimental validation, we applied this sensor to measure the pulse waveforms at various positions and depths of the radial artery in the wrist according to the diagnostic requirements. The results demonstrate the high feasibility of using optical systems for physiological measurement and using this FBG sensor for radial artery pulse waveform in clinical applications.

Human physical activity recognition is widely used in medical diagnosis, well-being management, and rehabilitation treatment. In spite of various Internet of Things (IoT) designs available in the literature, power resources often limit the lifetime of IoT. Regarding this weakness, this paper develops a new motion sensor system in wearable IoT (WIoT) for human physical activity recognition without any signal conditioning circuits. The triboelectricity-based physical model is explored in designing the motion sensor. It enables to collect motion signals caused by physical activities without any power supply. In addition, the triboelectric structure can be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion and a relatively stable voltage when involving continuous activities. Such a new design lays the foundations for constructing the next generation self-powered WIoT systems. Our new design has been extensively evaluated, where most common activities including sitting and standing, walking, climbing upstairs and downstairs, and running are used. The experimental results demonstrate that our system can achieve similar comparable performance as the state of the art for physical activity recognition at an average successful accuracy of over 80%. At the same time, our system reduces more than 25% energy consumption of the entire sensing hardware system which includes the sensor, microcontroller, and corresponding circuits.

In this article, a synchronized switching harvesting on inductor (SSHI)-based rectifier for triboelectric energy harvesting is reported for the first time. In the recent few years, triboelectric energy harvesters (TEs), also called triboelectric nanogenerators (TENGs), are emerging and extensively studied, which provide promising solutions for converting mechanical energy to electricity as power resources for electronics. To practically power electronics from TENGs, a power management module with high energy efficiency is also essential. The state of the art of power management circuits for triboelectric energy harvesting is mainly focused on increasing dc-dc power efficiency technically. In this article, we report an SSHI rectifying strategy associated with TEH design and provide a new perspective on designing TEHs or TENGs by considering their capacitance concurrently. A new theoretical model is developed for electricity generation from triboelectric energy harvesting considering the introduced pairing capacitance and the impact force under practical condition. We also demonstrate that ultralow-cost, easy-fabricated TEHs can also generate a reasonable amount of power. The experimental results show that the proposed SSHI rectifier increases the harvested power by 242.83% when compared with a full-wave bridge rectifier for a newly designed low-cost TEH. The proposed SSHI interface provides a promising strategy of rectifier design to enhance ac-dc energy efficiency for triboelectric energy harvesting.

Wearable robotics, also called exoskeletons, have been engineered for human-centered assistance for decades. They provide assistive technologies for maintaining and improving patients’ natural capabilities towards self-independence and also enable new therapy solutions for rehabilitation towards pervasive health. Upper limb exoskeletons can significantly enhance human manipulation with environments, which is crucial to patients’ independence, self-esteem, and quality of life. For long-term use in both in-hospital and at-home settings, there are still needs for new technologies with high comfort, biocompatibility, and operability. The recent progress in soft robotics has initiated soft exoskeletons (also called exosuits), which are based on controllable and compliant materials and structures. Remarkable literature reviews have been performed for rigid exoskeletons ranging from robot design to different practical applications. Due to the emerging state, few have been focused on soft upper limb exoskeletons. This paper aims to provide a systematic review of the recent progress in wearable upper limb robotics including both rigid and soft exoskeletons with a focus on their designs and applications in various pervasive healthcare settings. The technical needs for wearable robots are carefully reviewed and the assistance and rehabilitation that can be enhanced by wearable robotics are particularly discussed. The knowledge from rigid wearable robots may provide practical experience and inspire new ideas for soft exoskeleton designs. We also discuss the challenges and opportunities of wearable assistive robotics for pervasive health.

Landslides account for approximately 5% of natural disasters resulting in significant socio-economic impacts. As a major infrastructure issue, slope stability has been traditionally analyzed with multiple deterministic and probabilistic methods to evaluate the stability of slopes or the probability of landslides. Geotechnical engineers tend to visit the sites of slopes, measure the geometry and soil properties, and use those traditional methods to analyze the slope stability and provide a factor of safety evaluation and recommendation. The fast-growing new technologies such as the internet of things and big data analytics provide new directions for natural hazard prevention. This study is the first to use deep learning as a new method for slope stability analysis for landslide prevention. A convolutional neural network was used to establish the model via transfer learning for processing simulated slope images. After training, our model can accurately predict the factor of safety of slopes for new slope images. Our proposed method was validated by comparing it with a classic limit equilibrium method, i.e., the simplified Bishop method, which is widely used in commercial programs for slope stability analysis. The comparison results showed that our proposed deep learning method outperformed the traditional method by decreasing the computation time by orders of magnitude without sacrificing accuracy. The results demonstrated the possibility and advantages of using deep learning as a new type of slope stability analysis method, including its ability to analyze raw image data directly, high level of automation, satisfactory accuracy, and short computing time, which will enable onsite evaluation for slope stability analysis. Thus, it facilitates fast in-situ decision-making for geotechnical applications and ensures the feasibility of using the internet of things and big data analytics for natural hazard prevention.