As the world is marching into the era of the Internet of things (IoTs) and artificial intelligence (AI), the most vital requirement for reliable hardware development is an ultrafast response time and no performance degradation. As a reliable indicator of human physiological health, blood pressure measurement is vital in humans’ daily lives, which creates a huge demand in monitoring and diagnosing blood pressure problems. The triboelectric nanogenerator (TENG) is one of the best energy devices and healthcare applications in the new era since triboelectrification is a universal and ubiquitous effect with an abundant choice of materials. TENG is reliable in physiological monitoring applications and has many benefits, including being inexpensive, easy to manufacture, and lightweight, having self-powered properties, and being available in a wide range of materials. In this review, triboelectric nanogenerator based wrist pulse measurement was summarized for blood pressure monitoring and diagnosis applications. As per the Ayurveda, imbalance in three essential components of the wrist pulse implies the human health status and reveals symptoms for diseases. The design of different TENG-based blood pressure sensors, sensing mechanisms, performance, merits, and demerits of each method are discussed.

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Among the wearable sensor family, the triboelectric nanogenerator has excellent potential in human healthcare systems due to its small size, self-powered, and low cost. Here is the design and simulation of the triboelectric nanogenerator using the 3D model in COMSOL Multiphysics software for blood pressure measurement. As a reliable indicator of human physiological health, blood pressure (BP) has been utilized in more and more cases to predict and diagnose potential diseases and the dysfunction caused by hypertension. The main focus of this study is to prognosis and preserve human health against BP. It is one of the significant challenges in predicting and diagnosing BP in the human lifestyle. The self-powered triboelectric nanogenerator can diagnose BP using the wrist pulse pressure. To optimize the performance of the modeled triboelectric nanogenerator, the known wrist pulse pressure is applied explicitly, which converts the applied pressure into an equivalent electrical signal across the output terminals. An output open circuit voltage for the applied pulse pressure is 26 V. The generated output electrical signal is proportional to the applied pulse pressure, which is used to know the BP range. It ensures that the triboelectric nanogenerator is an opted sensor to sense the minute nadi pressure signal. This work validates that the simulated model has the potential to act as several health care monitors such as respiratory rate, heart rate, glucose range, joint motion sensing, gait, and CO 2 detectors.

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Upper limb impairment following stroke is often characterized by limited voluntary control in the affected arm. In addition, significant motor coordination problems occur on the unaffected arm due to avoidance of performing bilateral symmetrical activities. Rehabilitation strategies should, therefore, not only aim at improving voluntary control on the affected arm, but also contribute to synchronizing activity from both upper limbs. The encoder-controlled functional electrical stimulator, described in this paper, implements precise contralateral control of wrist flexion and extension with electrical stimulation. The stimulator is calibrated for each individual to obtain a table of stimulation parameters versus wrist angle. This table is used to set stimulation parameters dynamically, based on the difference in wrist angle between the set and stimulated side, which is continuously monitored. This allows the wrist on the stimulated side to follow flexion and extension patterns on the set side, thereby mirroring wrist movements of the normal side. This device also gives real-time graphical feedback on how the stimulated wrist is performing in comparison to the normal side. A study was performed on 25 normal volunteers to determine how closely wrist movements on the set side were being followed on the stimulated side. Graphical results show that there were minor differences, which were quantified by considering the peak angles of flexion and extension on the set and stimulated side for each participant. The mean difference in peak flexion and extension range of movement was 2.3 degrees and 1.9 degrees, respectively, with a mean time lag of 1 s between the set and the stimulated angle graphs.