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With the rapid development of touch sensing technology, keystroke authentication has been proved to be a more secure and reliable identification method. However, emotion has a big influence on users’ keystroke habits, a condition not considered in previous studies, yet greatly affect the accuracy of keystroke authentication. In this article, we propose a new strategy for keystroke authentication applicable to multiple emotional states based on piezoelectric touch panel, which provides precise force data and enables a higher detection accuracy. In our experiment, keystroke samples composed by force and time features of legitimate user and intruders in different emotions were collected by a piezoelectric touch screen. Four typical machine learning algorithms were used for authentication. Finally, the result was demonstrated, that Random Forest classifier achieved an accuracy of 96.40%, a FAR of 1.02% and a FRR of 8.82%, which is significantly better than the result when only emotion-stable keystroke samples were used to train classifiers. The proposed strategy improves the practicability of keystroke authentication technique in real world application. [ABSTRACT FROM AUTHOR]

Gait analysis is important in the field of healthcare, due to its close relationship to chronic diseases. With the development of the Internet of Health Things (IoHT), long-term gait monitoring and corresponding analysis can be performed remotely, reducing a patient’s time and traffic cost, while providing doctors more valuable gait information. In this paper, we present a piezoelectric insole gait monitoring system and its use in an IoHT architecture. Through the experimental results, the high detection sensitivity of 3.56 kPa and responsivity of 2.35 mV/kPa are achieved. Besides, computer software is developed with Qt platform, and the cloud database is also established. The plantar pressure distribution and center of pressure (CoP) trajectory are provided for further analysis, thereby satisfying the need for analyzing various diseases. Furthermore, the assembled system can continuously work for 16 hours, indicating its successful utilization when long-term gait monitoring is required. The presented work provides a feasible means for real-time, long-term, and accurate gait monitoring, prompting the development of gait analysis in the IoHT. [ABSTRACT FROM AUTHOR]

Benefiting from the development of the Internet of Healthcare Things (IoHT) in recent years, locomotion mode recognition using wearable sensors plays a more and more important role in the field of in-home rehabilitation. In this paper, a smart sensing system utilizing flexible electromyography (EMG) sensors and a forty-eight-channel plantar stress distribution sensor for locomotion mode recognition is presented, together with its use under the IoHT architecture. EMG and plantar stress distribution (PSD) information from ten healthy subjects in five common locomotion modes in daily life were collected, analyzed, and then transmitted to remote end terminals (e.g., personal computers). The data analysis process was implemented with machine learning techniques, through which the locomotion modes were determined with a high accuracy of 96.53%. This article demonstrates a feasible means for accurate locomotion mode recognition by combining wearable sensing techniques and the machine learning algorithm, potentially advancing the development for IoHT based in-home rehabilitation. [ABSTRACT FROM AUTHOR]

In the era of information technology, secure access to electronic systems is important for protecting private information. Among various authentication techniques, keystroke authentication methods based on users’ touch behavior have received increasing attention. In this paper, we present a technique for obtaining high multi-user authentication accuracy by utilizing users’ touch time and force information, which were obtained from a piezoelectric touch panel. After feeding a support vector machine (SVM) with users’ touch features, a high classification accuracy of 97% is achieved, validating the feasibility of the proposed technique for achieving highly secure multi-user authentication, hence advancing the development of security techniques. [ABSTRACT FROM AUTHOR]

Acupuncture is one of the most significant therapies of Chinese traditional medical science, and it is now globally utilized for diverse treatments, e.g., pain management. Traditionally, there is no quantification means for storing masters’ skills and examining trainee’s learning effect, hence, strongly limiting the development of acupuncture. For addressing this issue, in this article, a piezoelectric glove based wearable stress sensing technique is presented. Experimental results showcase that through the piezoelectric force-sensing glove, key parameters (e.g., peak stress at needle) during performing acupuncture are detected and extracted. Simultaneously, the Leap Motion, as a finger gesture monitoring technique, is utilized to capture the finger motion during acupuncture, allowing trainees to have a straightforward understanding of how gesture details are correlated to the stress information. The technique presented in this article potentially improves the learning efficiency of trainees and therefore advancing the progress of acupuncture. [ABSTRACT FROM AUTHOR]

Touch orientation detection is important for piezoelectric touch panels to stabilize force-voltage responsivities. Current touch orientation estimation techniques utilize machine learning algorithms for orientation classification. However, environmental noise could weaken the data quality which will result in a lowered detection accuracy. To address this issue, in this article, we present a noise robustness technique, in which different levels of noise data are injected into the training data. The performance of “dirty” data trained model exhibits a good performance (average mean absolute error (MAE) of 7.8 degrees) among signal-to-noise ratio (SNR) from 3 dB to 40 dB, indicating that an improved user experience can be obtained. [ABSTRACT FROM AUTHOR]

Concurrent high force detection accuracy and extended battery lifetime are strongly expected in wearable gait monitoring systems, which are essential for many Internet of Health Things (IoHT) applications. In this article, a piezoelectric insole device and a rectifying circuitry-based technique is presented to achieve these two ultimate goals. Here, walking induced positive and negative charges are separated for plantar stress detecting and energy harvesting, respectively; these two functions are realized concurrently. Experimental results demonstrate that first, the high detection sensitivity of 55 mN and responsivity of 231 mV/N are achieved, satisfying the need for diagnosing various diseases; second, the electric energy of 9.6 pJ ($2.67\times 10^{-15}\,\,\text{W}\cdot $ hr) is stored during a walking cycle, showing the potential of extending the battery lifetime. The developed technique enhances the development of gait monitoring in IoHT. [ABSTRACT FROM AUTHOR]

Artificial smart skins capable of interacting with people and sensing environmental stimuli have become a research topic in humanoid robotic applications. However, previously reported architectures suffer difficulties in achieving concurrent multi-dimensional sensing in a simple structure with low system costs. To address this issue, in this paper, an artificial smart skin constructed with polyimide/copper/polyvinylidene fluoride (PVDF) is presented for detecting 2D-position, proximity, dynamic force, and humidity via a smart combination of piezoelectric- and capacitive-effects. Moreover, a follow-up filtering algorithm is proposed to separate and calculate the stimuli associated with capacitance changes (position, proximity, and humidity). The proposed system achieves overall force and capacitive sensitivities of 0.05 N and 7 fF, a humidity responsivity at 0.22%/RH% over a relative humidity range of 10%–90% RH, a spatial resolution of 0.29 mm. This simple-structured device supports multiple functions with low system costs, thus advancing the field of robotics smart skins. [ABSTRACT FROM AUTHOR]

With the rapid development of electric and information technologies, security issues are becoming a pressing concern. This has given rise to a plethora of authentication techniques, leading among them being the fixed-password method due to its simple mechanism and independence from any specific hardware in the modern smartphone. While fixed-password methods enjoy benefits such as ease of use, potential security issues associated with password leakage cannot be ignored. This article, investigates an alternative strategy based on user keystrokes. Here, the user touch times and force features are extracted from a piezoelectric force touch panel which is an integral part of the hardware. Three broadly adopted machine learning classifiers are used for the collected data set, finally achieving an Equal Error Rate (EER) of 0.720%. [ABSTRACT FROM AUTHOR]

Human-machine interfaces (HMI) based on piezoelectric sensors have been receiving increasing attention due to their high force detection accuracy and low energy consumption, along with the ability to deploy user-oriented force sensing capability enabled by artificial intelligence. However, reports to date use a large amount of data to construct a customized model, which reduces user experience. To address this issue, an ensemble learning-based technique is presented in this paper, for building a customized classification model with a much lower data set. Experimental results demonstrate high force detection accuracy (98.32%) at low computational cost and at data collection times of less than a minute. This implies much fewer user operations yet enhancing the user experiences of HMI. [ABSTRACT FROM AUTHOR]

Touch-based interactivity has become an important function in displays. This paper reports on the signal processing of touch signals in which touch interactivity is processed as an image, and correlated double sampling (CDS) algorithm is applied for both common-mode noise reduction and global multivalued offset cancellation. Based on experimental results, we achieved a boost in SNR of 7.6 dB. The processed signal reduces detection errors and power consumption of the system. [ABSTRACT FROM PUBLISHER]