Analysis of a direct microcontroller interface for capacitively coupled resistive sensors

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. DOI:10.1109/TIM.2020.3034969A novel approach to directly interface a capacitively coupled resistive sensor to a microcontroller is presented in this article. The existing measurement schemes for such sensors are complex. In addition, the coupling capacitance often also holds important data. The proposed simple measurement system, for such series RC sensors, is capable of measuring both the resistance and the coupling capacitance. A detailed analysis on the effect of the nonidealities on the resistance measurement showed that it is independent of the accuracy of the charging capacitor, supply voltage, and preset threshold voltagePostprint (published version

Hand motion analysis during the execution of the action research arm test using multiple sensors

The Action Research Arm Test (ARAT) is a standardized outcome measure that can be improved by integrating sensors for hand motion analysis. The purpose of this study is to measure the flexion angle of the finger joints and fingertip forces during the performance of three subscales (Grasp, Grip, and Pinch) of the ARAT, using a data glove (CyberGlove II®) and five force-sensing resistors (FSRs) simultaneously. An experimental study was carried out with 25 healthy subjects (right-handed). The results showed that the mean flexion angles of the finger joints required to perform the 16 activities were Thumb (Carpometacarpal Joint (CMC) 28.56°, Metacarpophalangeal Joint (MCP) 26.84°, and Interphalangeal Joint (IP) 13.23°), Index (MCP 46.18°, Index Proximal Interphalangeal Joint (PIP) 38.89°), Middle (MCP 47.5°, PIP 42.62°), Ring (MCP 44.09°, PIP 39.22°), and Little (MCP 31.50°, PIP 22.10°). The averaged fingertip force exerted in the Grasp Subscale was 8.2 N, in Grip subscale 6.61 N and Pinch subscale 3.89 N. These results suggest that the integration of multiple sensors during the performance of the ARAT has clinical relevance, allowing therapists and other health professionals to perform a more sensitive, objective, and quantitative assessment of the hand function.Postprint (published version

Learning for a robot:deep reinforcement learning, imitation learning, transfer learning

Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed

Assessment of Hand Gestures Using Wearable Sensors and Fuzzy Logic

Hand dexterity and motor control are critical in our everyday lives because a significant portion of the daily motions we perform are with our hands and require some degree of repetition and skill. Therefore, development of technologies for hand and extremity rehabilitation is a significant area of research that will directly help patients recovering from hand debilities sustained from causes ranging from stroke and Parkinson’s disease to trauma and common injuries. Cyclic activity recognition and assessment is appropriate for hand and extremity rehabilitation because a majority of our essential motions are cyclic in their nature. For a patient on the road to regaining functional independence with daily skills, the improvement in cyclic motions constitutes an important and quantifiable rehabilitation goal. However, challenges exist with hand rehabilitation sensor technologies preventing acquisition of long-term, continuous, accurate and actionable motion data. These challenges include complicated and uncomfortable system assemblies, and a lack of integration with consumer electronics for easy readout. In our research, we have developed a glove based system where the inertial measurement unit (IMU) sensors are used synergistically with the flexible sensors to minimize the number of IMU sensors. The classification capability of our system is improved by utilizing a fuzzy logic data analysis algorithm. We tested a total of 25 different subjects using a glove-based apparatus to gather data on two-dimensional motions with one accelerometer and three-dimensional motions with one accelerometer and two flexible sensors. Our research provides an approach that has the potential to utilize both activity recognition and activity assessment using simple sensor systems to help patients recover and improve their overall quality of life

A new IMMU-based data glove for hand motion capture with optimized sensor layout

The number of people with hand disabilities caused by stroke is increasing every year. Developing a low-cost and easy-to-use data glove to capture the human hand motion can be used to assess the patient’s hand ability in home environment. While a majority of existing hand motion capture methods are too complex to be used for patients in residential settings. This paper proposes a new sensor layout strategy using the inertial and magnetic measurement units and designs a multi-sensor Kalman data fusion algorithm. The sensor layout strategy is optimized according to the inverse kinematics and the developed hand model, and the number of sensors can be significantly reduced from 12 in conventional systems to 6 in our system with the hand motion being completely and accurately reconstructed. Hand motion capture experiments were conducted on a healthy subject using the developed data glove. The hand motion can be restored completely and the hand gesture can be recognized with an accuracy of 85%. The results of a continuous hand movement indicate an average error under 15% compared with the common glove with full sensors. This new set with optimized sensor layout is promising for lower-cost and residential medical applications