Emotion recognition, a process of automatic cognition of human emotions, has great potential to improve the degree of social intelligence. Among various recognition methods, Emotion recognition based on touch event’s temporal and force information receives global interests. Although previous studies have shown promise in the field of keystroke-based emotion recognition, they are limited by the need for long-term text input and the lack of high-precision force sensing technology, hindering their real-time performance and wider applicability. To address this issue, in this paper, a piezoelectric-based keystroke dynamic technique is presented for quick emotion detection. The nature of piezoelectric materials enables high-resolution force detection. Meanwhile, the data collecting procedure is highly simplified because only the password entry is needed. International Affective Digitized Sounds (IADS) are applied to elicit users’ emotions, and a PAD emotion scale is used to evaluate and label the degree of emotion induction. A Random Forest (RF) based algorithm is used in order to reduce the training dataset and improve algorithm portability. Finally, an average recognition accuracy of 79.37% of 4 emotions (happiness, sadness, fear, disgust) is experimentally achieved. The proposed technique improves the reliability and practicability of emotion recognition in realistic social systems.

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnosis carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement units (IMUs) based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML) based techniques have been proposed to smartly map IMU captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. To help readers comprehensively understand the fundamentals and state-of-the-art techniques, this article systematically reviews this field, by introducing and explaining relevant application scenarios, discussing challenges, and predicting foreseeable future trends.

Neurological and orthopedic chronic diseases are proven to numerous complications and related dysfunction for patients, and are leading factors of death and disability. Conventional methods of diagnosis and rehabilitation are conducted at hospitals. However, due to the aging society and controversial privacy protection, traditional methods implemented in medical institutions have faced numerous challenges. Hence, internet of healthcare things (IoHT) based techniques have recently begun to be implemented. In order to provide flexible utilization and secure privacy for patients, wearable devices, like whole body devices based on electromyography (EMG) and radio frequency identification (RFID) insole systems, based on gait-analysis were applied. Among these techniques, insole systems for medical analysis are more suitable, because the root cause of neurologic and orthopedic chronic diseases are directly related to several gait characteristics. Neurologic and orthopedic chronic diseases are caused by lesions of the central nervous system (CNS), peripheral nerves (PN) and orthopedic limbs, similarly gait features are comprehensive results generated by the coordination of CNS, PN and lower limbs However, owing to lack of standards for instrumentation and the knowledge gap between engineering and pathogenic mechanism, the medical application of insole systems and the development of IoHT are hindered. This article is presented to address these issues. In this context, five plantar sensing mechanisms and their related products will be overviewed. Next, pathogenic mechanisms and pathological gait features of six representative chronic neurologic diseases will be displayed. Likewise, the development of corresponding insole systems for supervision and recovery for these diseases will be reviewed as well. Finally, challenges and future trends for current work will be discussed. This article will not only provide guidelines for the design of insole systems targeted for specific diseases but will also inspire the innovation of novel ideas.

Patients physically handicapped can't take care of themselves. Helping them easily control the objects around them will reduce their psychological burden and social pressure. In this article, a semi-autonomous grasping system based on eye movement and EEG is presented to achieve this goal. Patient just needs to gaze the target object and keep focused, the manipulator will automatically move to its position and grasp it. Experimental results verify the reliability of the system. This system promotes the development of human-computer interaction system based on multi-sensor fusion.

Piezoelectric touch sensing in interactive displays gains increasing attentions due to its high force detection sensitivity and intrinsic mechanical-to-electrical conversion ability. However, the instable force-voltage responsivity induced by preload effect of piezoelectric materials reduces the force detection accuracy of secondary force touches, which is important to touch and haptic applications such as peek-pop. To address this issue, in this article, we present a preload effect elimination technique, in which the relationship between the piezoelectric coefficients and static preload is studied first, and then the detected secondary force touch is calibrated by using the previously applied static force information. Experimental results demonstrate that the force detection accuracy is boosted by 15.17% after applying the developed technique to secondary force touches with different preload values, potentially allowing the system to precisely interpret secondary force touch amplitude and hence enhancing the development of touch sensing in interactive displays.

In order to quantify the manipulation process of acupuncture, in this article, a piezoelectric glove based wearable stress sensing system is presented. Served as the sensitive element with small volume and high tensile resistance, PVDF greatly meet the need of quantitative analysis. Through piezoelectric force sensing glove, the system is capable of detecting both perpendicular stress as well as shear stress. Besides, key parameters including peak stress at needle are detected and extracted, potentially allowing for a higher learning efficiency hence advancing the development of acupuncture.

In this article, we propose a new sensor architecture, in which electrodes are settled at the edge of the touch panel, ensuring a very high optical transmittance. The touch event detection relies on electrical capacitance tomography (ECT) technique. The presented technique provides a feasible means to boost the optical transmittance of the touch panel layer while maintaining touch detection accuracy.

Concurrent high force detection accuracy and extended battery lifetime are expected for wearable gait monitoring systems. In this article, a piezoelectric insole device and rectifying circuitry-based technique is presented to achieve these two goals. Here, walking induced positive and negative charges are separated for plantar stress detection and energy harvesting respectively, realizing the two functions concurrently. The high detection sensitivity of 55 mN and responsivity of 231 mV/N are achieved, satisfying the need for diagnosing various diseases. 1.6 pJ is stored during a walking event, extending the battery lifetime. The developed technique enhances the development of gait monitoring in IoHT.

In Internet of Things (IoT) applications, among various authentication techniques, keystroke authentication methods based on a user’s touch behavior have received increasing attention, due to their unique benefits. In this paper, we present a technique for obtaining high user authentication accuracy by utilizing a user’s touch time and force information, which are obtained from an assembled piezoelectric touch panel. After combining artificial neural networks with the user’s touch features, an equal error rate (EER) of 1.09% is achieved, and hence advancing the development of security techniques in the field of IoT.

Piezoelectric force touch panels are extensively utilized as human-machine interfaces for 3-dimensional touch sensing in internet of things (IoT) applications. However, the unstable force voltage responsivity issue induced by different touch orientations limits the successful use of piezoelectric touch panels. In this article, a piezoelectric touch panel, which is sensitive to both capacitive and force stimulation, is assembled; and a touch orientation classification technique is developed to calibrate the detected force amplitude by training a machine learning model with finger induced capacitive information. Finally, a high stable force voltage responsivity of 87.5% is achieved experimentally.

In order to quantify the manipulation process of acupuncture, in this article, a piezoelectric glove based wearable stress sensing system is presented. Served as the sensitive element with small volume and high tensile resistance, PVDF greatly meet the need of quantitative analysis. Through piezoelectric force sensing glove, the system is capable of detecting both perpendicular stress as well as shear stress. Besides, key parameters including peak stress at needle are detected and extracted, potentially allowing for a higher learning efficiency hence advancing the development of acupuncture.

Previously reported artificial smart skins for humanoid robots suffer difficulties in achieving multi-dimensional sensing in a simple structure with a low system cost. In this paper, we exhibit a polyimide/copper/PVDF structured smart skin for multidimensional sensing (including position, proximity, force and humidity), which is enabled by smart utilization of dielectric property of polyimide layer and piezoelectric of the PVDF layer. Experimental results demonstrate that the developed technique successfully obtains large area, flexibility, and multiple-function with simple device structure and low component cost, potentially enabling massive production and advanced services for the humanoid robots’ applications.