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The integration of Electric Power Steering (EPS) in automobiles is crucial to reduce the steering effort for drivers while ensuring optimal steering performance to aid in vehicle control. The EPS system mandates a minimum 2Nm torque input from the driver to initiate steering wheel movement in all conditions. Key components of the electric power steering system include the torque sensor, DC motor assistance, current sensor, and an Electronic Control Unit (ECU). This study is dedicated to mathematically modeling and characterizing the EPS System. We have developed a mathematical model for column-assisted front-wheel steering using Newton's equations of motion. The controller for our model was designed based on assist characteristic curves and algorithmic control. Test results demonstrate that our self-designed ESP controller, utilizing an innovative motor control approach, significantly enhances ease of steering and controllability perception. Furthermore, it meets the specified criteria for steering performance.

Recently, posture recognition technology has advanced rapidly. Herein, we present a novel posture angle calculation system utilizing a single inertial measurement unit and a spatial geometric equation to accurately identify the three-dimensional (3D) motion angles and postures of both the upper and lower limbs of the human body. This wearable system facilitates continuous monitoring of body movements without the spatial limitations or occlusion issues associated with camera-based methods. This posture-recognition system has many benefits. Providing precise posture change information helps users assess the accuracy of their movements, prevent sports injuries, and enhance sports performance. This system employs a single inertial sensor, coupled with a filtering mechanism, to calculate the sensor’s trajectory and coordinates in 3D space. Subsequently, the spatial geometry equation devised herein accurately computed the joint angles for changing body postures. To validate its effectiveness, the joint angles estimated from the proposed system were compared with those from dual inertial sensors and image recognition technology. The joint angle discrepancies for this system were within 10° and 5° when compared with dual inertial sensors and image recognition technology, respectively. Such reliability and accuracy of the proposed angle estimation system make it a valuable reference for assessing joint angles.

Observation of human joint motion plays an important role in many fields. The results of the human links can provide information about musculoskeletal parameters. Some devices can track real-time joint movement in the human body during essential daily activities, sports, and rehabilitation with memory for storing the information concerning the body. Based on the algorithm for signal features, the collected data can reveal the conditions of multiple physical and mental health issues. This study proposes a novel method for monitoring human joint motion at a low cost. We propose a mathematical model to analyze and simulate the joint motion of a human body. The model can be applied to an Inertial Measurement Unit (IMU) device for tracking dynamic joint motion of a human. Finally, the combination of image-processing technology was used to verify the results of model estimation. Moreover, the verification showed that the proposed method can estimate joint motions properly with reduced-number IMUs.

Inertial Measurement Units (IMUs)-based gait analysis is a promising and attractive approach for user recognition. Recently, the adoption of deep learning techniques has gained significant performance improvement. However, most existing studies focused on exploiting the spatial information of gait data (using Convolutional Neural Network (CNN)) while the temporal part received little attention. In this study, we propose a new multi-model Long Short-term Memory (LSTM) network for learning the gait temporal features. First, we observe that LSTM is able to capture the pattern hidden inside the gait data sequences that are out-of-synchronization. Thus, instead of using the gait cycle-based segment, our model accepts the gait cycle-free segment (i.e., fixed-length window) as the input. By this, the classification task does not depend on the gait cycle detection task, which usually suffers from noise and bias. Second, we propose a new LSTM network architecture, in which, one LSTM is used for each gait data channel and a group of consecutive signals is processed in each step. This strategy allows the network to effectively handle the long input data sequence and achieve improved performance compared to existing LSTM-based gait models. In addition, besides using the LSTM alone, we extend it by combining with a CNN model to construct a hybrid network, which further improves the recognition performance. We evaluated our LSTM and hybrid networks under different settings using the whuGAIT and OU-ISIR datasets. The experiments showed that our LSTM network outperformed the existing LSTM networks, and its combination with CNN established new state-of-the-art performance on both the verification and identification tasks.

Digital data certified by a reputable organization are valuable digital data that can be stored or shared on the internet. However, the problems are: (1) How to ensure the anonymity of organizations on issued certificates? (2) How to ensure that valuable digital data are securely stored in the system? and (3) How could people verify the reliability of shared data while still ensuring the confidentiality of its content, and how to ensure that the data sharing process is safe, transparent, and fair? Therefore, we propose data producing, data storing, and data sharing schemas. In the data producing schema, we deploy a group signature scheme for a group of reputable organizations that provide the same type of service, an organization in the group generates a valuable digital data from raw data sent from a data owner and then issues a certificate on the ciphertext of this digital data. In the data storing schema, the data owner uploads his/her data to the public Inter-Planetary File System network and then stores the access address of the stored data and the corresponding certificate on the blockchain ledger. In the data sharing schema, everyone on the system could verify the reliability of shared data before sending a data sharing request to the data owner. The data sharing process is performed via a smart contract, and involved parties have to escrow to encourage honesty. The schemas of data storing and sharing guarantee the security properties including confidentiality, integrity, privacy, non-repudiation, and anonymity.

Plasmonic nanopatch antennas that incorporate dielectric gaps hundreds of picometers to several nanometers thick have drawn increasing attention over the past decade because they confine electromagnetic fields to grossly sub-diffraction-limited volumes. Substantial control over the optical properties of excitons and color centers confined within these plasmonic cavities has already been demonstrated with far-field optical spectroscopies, but near-field optical spectroscopies are essential for an improved understanding of the plasmon–emitter interaction at the nanoscale. Here, we characterize the intensity and phase-resolved plasmonic response of isolated nanopatch antennas by cathodoluminescence microscopy. Furthermore, we explore the distinction between optical and electron beam spectroscopies of coupled plasmon–exciton heterostructures to identify constraints and opportunities for future nanoscale characterization and control of hybrid nanophotonic structures. While we observe substantial Purcell enhancement in time-resolved photoluminescence spectroscopies, negligible Purcell enhancement is observed in cathodoluminescence spectroscopies of hybrid nanophotonic structures. The substantial differences in measured Purcell enhancement for electron beam and laser excitation can be understood as a result of the different selection rules for these complementary experiments. These results provide a fundamentally new understanding of near-field plasmon–exciton interactions in nanopatch antennas, which is essential for myriad emerging quantum photonic devices.

Skeletal muscles require fitness and rehsabilitation exercises to develop. This paper presents a method to observe and evaluate the conditions of muscle extension. Based on theories about the muscles and factors that affect them during leg contraction, an electromyography (EMG) sensor was used to capture EMG signals. The signals were applied by signal processing with the wavelet packet entropy method. Not only did the experiment follow fitness rules to obtain correct EMG signal of leg extension, but the combination of inertial measurement unit (IMU) sensor also verified the muscle state to distinguish the muscle between non-fatigue and fatigue. The results show the EMG changing in the non-fatigue, fatigue, and calf muscle conditions. Additionally, we created algorithms that can successfully sense a user’s muscle conditions during exercise in a leg extension machine, and an evaluation of condition sensing was also conducted. This study provides proof of concept that EMG signals for the sensing of muscle fatigue. Therefore, muscle conditions can be further monitored in exercise or rehabilitation exercise. With these results and experiences, the sensing methods can be extended to other smart exercise machines in the future.

Exercise is good for health, quality of life, and maintenance of human muscles. Dumbbells are popular indoor exercise equipment with several benefits such as low cost, high flexibility in space and time, easy operation, and suitability for people of all ages. Facilitated by advances in the Internet of Things, smart dumbbells that provide automatic counting and motion monitoring functions have been developed. To perform these tasks, the key process is identification of exercise mode. This study proposes a method to identify essential muscle groups’ (biceps, triceps, and deltoids) exercise modes of a dumbbell using an inertial measurement unit to provide three-axis angular velocities and accelerations. The motion angles were estimated from the axial acceleration and angular velocity. Phase diagrams and time plots of the axial angle, angular velocity, and acceleration were used to extract significant features of each exercise. Machine Learning and weighting functions were developed to combine these features into an identification index value for accurate identification and classification of the exercise modes. An algorithm was developed to verify the exercise mode identification. The results show that the proposed method and weighting function can successfully identify the six exercise modes. The identification algorithm was 99.5% accurate. The exercise mode identification of the dumbbell is confirmed.

Smart environments established by the development of mobile technology have brought vast benefits to human being. However, authentication mechanisms on portable smart devices, particularly conventional biometric based approaches, still remain security and privacy concerns. These traditional systems are mostly based on pattern recognition and machine learning algorithms, wherein original biometric templates or extracted features are stored under unconcealed form for performing matching with a new biometric sample in the authentication phase. In this paper, we propose a novel gait based authentication using biometric cryptosystem to enhance the system security and user privacy on the smart phone. Extracted gait features are merely used to biometrically encrypt a cryptographic key which is acted as the authentication factor. Gait signals are acquired by using an inertial sensor named accelerometer in the mobile device and error correcting codes are adopted to deal with the natural variation of gait measurements. We evaluate our proposed system on a dataset consisting of gait samples of 34 volunteers. We achieved the lowest false acceptance rate (FAR) and false rejection rate (FRR) of 3.92% and 11.76%, respectively, in terms of key length of 50 bits.

BackgroundVietnam has successfully reduced malaria incidence by more than 90% over the past 10 years, and is now preparing for malaria elimination. However, the remaining malaria burden resides in individuals that are hardest to reach, in highly remote areas, where many malaria cases are treated through the informal private sector and are not reported to public health systems. This qualitative study aimed to contextualize and characterize the role of private providers, care-seeking behaviour of individuals at high risk of malaria, as well as risk factors that should be addressed through malaria elimination programmes in Vietnam.MethodsSemi-structured qualitative interviews were conducted with 11 key informants in Hanoi, 30 providers, 9 potential patients, and 11 individuals at risk of malaria in Binh Phuoc and Kon Tum provinces. Audio recorded interviews were transcribed and uploaded to Atlas TI™, themes were identified, from which programmatic implications and recommendations were synthesized.ResultsQualitative interviews revealed that efforts for malaria elimination in Vietnam should concentrate on reaching highest-risk populations in remote areas as well their care providers, in particular private pharmacies, private clinics, and grocery stores. Among these private providers, diagnosis is currently based on symptoms, leaving unconfirmed cases that are not reported to public health surveillance systems. Among at-risk individuals, knowledge of malaria was limited, and individuals reported not taking full courses of treatment, a practice that threatens selection for drug resistance. Access to insecticide-treated hammock nets, a potentially important preventive measure for settings with outdoor biting Anopheles vectors, was also limited.ConclusionsMalaria elimination efforts in Vietnam can be accelerated by targeting improved treatment, diagnosis, and reporting practices to private pharmacies, private clinics, and grocery stores. Programmes should also seek to increase awareness and understanding of malaria among at-risk populations, in particular the importance of using preventive measures and adhering to complete courses of anti-malarial medicines.