Human Motion State Recognition Based on Flexible, Wearable Capacitive Pressure Sensors

Piezoelectric biomaterials for providing electrical stimulation in bone tissue engineering: Barium titanate

With the increasing clinical demand for orthopedic implants, bone tissue engineering based on a variety of bioactive materials has shown promising applications in bone repair. And various physiological cues, such as mechanical, electrical, and magnetic stimulation, can influence cell fate and participate in bone regeneration. Natural bone has a piezoelectric effect due to the non-centrosymmetric nature of collagen, which can aid in cell adhesion, proliferation and differentiation, and bone growth by converting mechanical stimuli into electrical stimuli. Piezoelectric materials have the same piezoelectric effect as human bone, and they are able to deform in response to physiological movement, thus providing electrical stimulation to cells or damaged tissue without the need for an external power source. Among them, Barium titanate (BaTiO3) is widely used in tumor therapy, tissue engineering, health detection and drug delivery because of its good biocompatibility, low cytotoxicity and good piezoelectric properties. This review describes the piezoelectric effect of natural bone and the characteristics of various types of piezoelectric materials, from the synthesis and physicochemical characteristics of BaTiO3 and its application in biomedicine. And it highlights the great potential of BaTiO3 as piezoelectric biomaterials in the field of bone tissue engineering in anticipation of providing new ideas and opportunities for researchers. The translational potential of this article: This review systematically discusses barium titanate, a bioactive material that can mimic the piezoelectric effect of natural bone tissue, which can intervene in the regenerative repair of bone by providing a sustained electrical microenvironment for bone repair scaffolds. This may help to solve the current problem of poor osteogenic properties of bioactive materials by utilizing barium titanate.

Morse Code Recognition Based on a Flexible Tactile Sensor with Carbon Nanotube/Polyurethane Sponge Material by the Long Short-Term Memory Model

Morse code recognition plays a very important role in the application of human-machine interaction. In this paper, based on the carbon nanotube (CNT) and polyurethane sponge (PUS) composite material, a flexible tactile CNT/PUS sensor with great piezoresistive characteristic is developed for detecting Morse code precisely. Thirty-six types of Morse code, including 26 letters (A-Z) and 10 numbers (0-9), are applied to the sensor. Each Morse code was repeated 60 times, and 2160 (36 × 60) groups of voltage time-sequential signals were collected to construct the dataset. Then, smoothing and normalization methods are used to preprocess and optimize the raw data. Based on that, the long short-term memory (LSTM) model with excellent feature extraction and self-adaptive ability is constructed to precisely recognize different types of Morse code detected by the sensor. The recognition accuracies of the 10-number Morse code, the 26-letter Morse code, and the whole 36-type Morse code are 99.17%, 95.37%, and 93.98%, respectively. Meanwhile, the Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) models are built to distinguish the 36-type Morse code (letters of A-Z and numbers of 0-9) based on the same dataset and achieve the accuracies of 91.37%, 88.88%, 87.04%, and 90.97%, respectively, which are all lower than the accuracy of 93.98% based on the LSTM model. All the experimental results show that the CNT/PUS sensor can detect the Morse code's tactile feature precisely, and the LSTM model has a very efficient property in recognizing Morse code detected by the CNT/PUS sensor.

Gesture Recognition Based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory Network for a Wearable Wrist Sensor with Multi-Walled Carbon Nanotube/Cotton Fabric Material

Flexible pressure sensors play a crucial role in detecting human motion and facilitating human-computer interaction. In this paper, a type of flexible pressure sensor unit with high sensitivity (2.242 kPa-1), fast response time (80 ms), and remarkable stability (1000 cycles) is proposed and fabricated by the multi-walled carbon nanotube (MWCNT)/cotton fabric (CF) material based on a dip-coating method. Six flexible pressure sensor units are integrated into a flexible wristband and made into a wearable and portable wrist sensor with favorable stability. Then, seven wrist gestures (Gesture Group #1), five letter gestures (Gesture Group #2), and eight sign language gestures (Gesture Group #3) are performed by wearing the wrist sensor, and the corresponding time sequence signals of the three gesture groups (#1, #2, and #3) from the wrist sensor are collected, respectively. To efficiently recognize different gestures from the three groups detected by the wrist sensor, a fusion network model combined with a convolutional neural network (CNN) and the bidirectional long short-term memory (BiLSTM) neural network, named CNN-BiLSTM, which has strong robustness and generalization ability, is constructed. The three types of Gesture Groups were recognized based on the CNN-BiLSTM model with accuracies of 99.40%, 95.00%, and 98.44%. Twenty gestures (merged by Group #1, #2, and #3) were recognized with an accuracy of 96.88% to validate the applicability of the wrist sensor based on this model for gesture recognition. The experimental results denote that the CNN-BiLSTM model has very efficient performance in recognizing different gestures collected from the flexible wrist sensor.

Evaluation of blood pressure using a flexible and wearable capacitive pressure sensor

In recent times, the high demand for flexible and wearable pressure sensors to monitor human health, particularly for patients afflicted with hypertension or high blood pressure (BP), has captured the keen interest of researchers. Capacitance-based flexible sensing devices offer real-time metrics regarding vital physiological parameters of the human body, such as BP and pulse rate (PR), thereby enabling the identification of cardiovascular complications. In this regard, we have developed a capacitive pressure sensor using polydimethylsiloxane (PDMS) and deionized water (DIW) and improved its key parameters by adding baking powder to PDMS-DIW. The sensor demonstrated excellent performance in static pressure measurements with a sensitivity of 0.021 Pa-1, detection limit of 1 Pa, and response time of 100 ms. We further investigated its application in human BP monitoring. The sensor successfully captured the oscillometric waveform (OMW) for all 160 participants and demonstrated excellent performance in accurately measuring BP, meeting all criteria outlined as the universal standard when compared with the reference devices: OMRON BP device and the gold-standard mercury-based sphygmomanometer. Furthermore, the sensor accurately provided the PR and agreed well with the reference BP device. Therefore, the developed BP sensor can be a viable alternative to replace the pressure sensors in existing BP devices.

Integration of Capacitive Pressure Sensor-on-Chip with Lead-Free Perovskite Solar Cells for Continuous Health Monitoring

The increasing prevalence of hypertension necessitates continuous blood pressure monitoring. This can be safely and painlessly achieved using non-invasive wearable electronic devices. However, the integration of analog, digital, and power electronics into a single system poses significant challenges. Therefore, we demonstrated a comprehensive multi-scale simulation of a sensor-on-chip that was based on a capacitive pressure sensor. Two analog interfacing circuits were proposed for a full-scale operation ranging from 0 V to 5 V, enabling efficient digital data processing. We also demonstrated the integration of lead-free perovskite solar cells as a mechanism for self-powering the sensor. The proposed system exhibits varying sensitivity from 1.4 × 10-3 to 0.095 (kPa)-1, depending on the pressure range of measurement. In the most optimal configuration, the system consumed 50.5 mW, encompassing a 6.487 mm2 area for the perovskite cell and a CMOS layout area of 1.78 × 1.232 mm2. These results underline the potential for such sensor-on-chip designs in future wearable health-monitoring technologies. Overall, this paper contributes to the field of wearable health-monitoring technologies by presenting a novel approach to self-powered blood pressure monitoring through the integration of capacitive pressure sensors, analog interfacing circuits, and lead-free perovskite solar cells.

A Piezoelectric MEMS Speaker with a Combined Function of a Silent Alarm

To explore the versatility of speakers, a piezoelectric micro-electro-mechanical system (MEMS) speaker combining the function of a silent alarm is proposed, which mainly comprises a lead zirconate titanate (PZT) actuation layer and a rigid-flexible coupling supporting layer. Measurements performed on encapsulated prototypes mounted to an artificial ear simulator have revealed that, compared to a speaker with a rigid supporting layer, the sound pressure level (SPL) of the proposed piezoelectric MEMS speaker with a rigid-flexible coupling supporting layer is significantly higher and is especially higher by 4.1-20.1 dB in the frequency range from 20 Hz to 4.2 kHz, indicating that the rigid-flexible coupling supporting layer can improve the SPL significantly in low frequency. Moreover, the spectral distribution characteristic of its playback audio is similar to that of the commercial electromagnetic type. The device can also function as a silent alarm based on oral airflows in dangerous situations, as it performs well at recognizing words according to their unique voltage-signal characteristics, and can avoid the effects of external sound noise, body movement, long distance, and occlusion. This strategy provides inspiration for functional diversification of piezoelectric MEMS speakers.

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders

Study on the objectivity of pulse diagnosis is inseparable from the instruments to obtain the pulse waves. The single-pulse diagnostic instrument is relatively mature in acquiring and analysing pulse waves, but the pulse information captured by single-pulse diagnostic instrument is limited. The sensor arrays can simulate rich sense of the doctor's fingers and catch multipoint and multiparameter array signals. How to analyse the acquired array signals is still a major problem in the objective research of pulse diagnosis. The goal of this study was to establish methods for analysing arrayed pulse waves and preliminarily apply them in hypertensive disorders. While a sensor array can be used for the real-time monitoring of twelve pulse wave channels, for each subject in this study, only the pulse wave signals of the left hand at the "guan" location were obtained. We calculated the average pulse wave (APW) per channel over a thirty-second interval. The most representative pulse wave (MRPW) and the APW were matched by their correlation coefficient (CC). The features of the MRPW and the features that corresponded to the array pulse volume (APV) parameters were identified manually. Finally, a clinical trial was conducted to detect these feature performance indicators in patients with hypertensive disorders. The independent-samples t-tests and the Mann-Whitney U-tests were performed to assess the differences in these pulse parameters between the healthy and hypertensive groups. We found that the radial passage (RP) APV h1, APV h3, APV h4, APV h3/h1 (P < 0.01), and APV h4/h1 (P < 0.05) were significantly higher in the hypertensive group than in the healthy group; the intermediate passage (IP) APV h4, APV h3/h1 (P < 0.05), and APV h4/h1 (P < 0.01) and the mean APV h3, APV h3/h1 (P < 0.05), and APV h4/h1 (P < 0.01) were significantly higher in the hypertensive group than in the healthy group, and the ulnar passage (UP) APV h4/h1 (P < 0.05) was clearly elevated in the hypertensive group. These results provide a preliminary validation of this novel approach for determining the APV by arrayed pulse wave analysis. In conclusion, we identified effective indicators of hypertensive vascular function. Traditional Chinese medicine (TCM) pulses comprise multidimensional information, and a sensor array could provide a better indication of TCM pulse characteristics. In this study, the validation of the arrayed pulse wave analysis demonstrates that the APV can reliably mirror TCM pulse characteristics.