A quantitative evaluation of kinetic parameters, the joint’s range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device’s positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user’s vital signs directly from the body in an accurate and non–invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach’s subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post–operative rehabilitation and athletes’ training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user’s health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.

/pdf/wearable-sensors-and-smart-devices-to-monitor-rehabilitation-jk0nhkw4.pdf

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

Flexible sensors are highly flexible, malleable, and capable of adapting todifferent shapes, surfaces, and environments, which opens a wide range ofpotential applications in the field of human-machine interface (HMI). Inparticular, flexible pressure sensors as a crucial member of the flexiblesensor family, are widely used in wearable devices, health monitoringinstruments, robots and other fields because they can achieve accuratemeasurement and convert the pressure into electrical signals. The mostintuitive feeling that flexible sensors bring to people is the change ofhuman-machine interface interaction, from the previous rigid interaction suchas keyboard and mouse to flexible interaction such as smart gloves, more inline with people's natural control habits. Many advanced flexible pressuresensors have emerged through extensive research and development, and to adaptto various fields of application. Researchers have been seeking to enhanceperformance of flexible pressure sensors through improving materials, sensingmechanisms, fabrication methods, and microstructures. This paper reviews the flexible pressure sensors in HMI in recent years, mainlyincluding the following aspects: current cutting-edge flexible pressuresensors; sensing mechanisms, substrate materials and active materials; sensorfabrication, performances, and their optimization methods; the flexiblepressure sensors for various HMI applications and their prospects. 

Flexible Pressure Sensors in Human-Machine Interface Applications.


Human–robot interaction (HRI) has escalated in notability in recent years, and multimodal communication and control strategies are necessitated to guarantee a secure, efficient, and intelligent HRI experience. In spite of the considerable focus on multimodal HRI, comprehensive disquisitions delineating various modalities and intricately analyzing their combinations remain elusive, consequently limiting holistic understanding and future advancements. This article aspires to bridge this inadequacy by conducting a profound exploration of multimodal HRI, predominantly concentrating on four principal modalities: vision, auditory and language, haptics, and physiological sensing. An extensive review encapsulating algorithmic dissection, interface devices, and applicative dimensions forms part of this discourse. This manuscript distinctively combines multimodal HRI with cognitive science, deeply probing into the three dimensions, perception, cognition, and action, thereby demystifying algorithms intrinsic to multimodal HRI. Finally, it accentuates the empirical challenges and contours preemptive trajectories for multimodal HRI in human‐centric smart manufacturing.

Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

A neural network based framework for variable impedance skills learning from demonstrations

Flexible electronic technology is one of the research hotspots, and numerous wearable devices have been widely used in our daily life. As an important part of wearable devices, flexible sensors can effectively detect various stimuli related to specific environments or biological species, having a very bright development prospect. Therefore, there has been lots of studies devoted to developing high-performance flexible pressure sensors. In addition to developing a variety of materials with excellent performances, the microstructure designs of materials can also effectively improve the performances of sensors, which has brought new ideas to scientists and attracted their attention increasingly. This paper will summarize the flexible pressure sensors based on material microstructure designs in recent years. The paper will mainly discuss the processing methods and characteristics of various sensors with different microstructures, and compare the advantages, disadvantages, and application scenarios of them. At the same time, the main application fields of flexible pressure sensors based on microstructure designs will be listed, and their future development and challenges will be discussed.

/pdf/research-progresses-in-microstructure-designs-of-flexible-qf83eeez.pdf

Research Progresses in Microstructure Designs of Flexible Pressure Sensors

This letter proposes a dynamic system approach to learn point-to-point motions while keeping the stability of the dynamic system. The proposed approach is grounded on a Learning from Demonstration (LfD) method based on a neural network, which gets a better reproduction performance while guaranteeing the generalization ability. The proposed approach has been experimentally validated on the LASA dataset and by the &#x201C;pick-and-place&#x201D; task of Franka Emika robot, and experimental results demonstrate that: (1) compared with the state-of-the-art results, the trajectory generated by the proposed approach achieves higher accuracy (approximately 24.79&#x0025;) in terms of the similarity with respect to the demonstration; (2) the proposed approach can handle high dimensional data and learn from one or more demonstrations; (3) the proposed approach can guarantee the performance regardless of the variation of starting points even in the case of high dimensional complex motions. 

Learning Accurate and Stable Point-to-Point Motions: A Dynamic System Approach

Robot-assisted thumb rehabilitation can improve the hand function of stroke patients because the thumb accounts for 40% of hand function. However, existing thumb rehabilitation robots are limited in terms of portability, comfort, adaptability, and independent joint actuation. This article proposes an untethered adaptive thumb exoskeleton that actively assists the 3-degree-of-freedom movements of the thumb. The exoskeleton is composed of an adaptive thumb mechanism and a spherical mechanism. The kinematics, statics, and performances of the adaptive thumb mechanism and the spherical mechanism are analyzed. Experimental validation is performed to test the workspace, self-alignment, interaction forces, admittance controller, and grasping assistance performance of the exoskeleton. The workspace and self-aligning experiments prove that the proposed exoskeleton can achieve a large workspace of the thumb joints and realize self-alignment. The interaction force experimental results show that the exoskeleton can reduce the tangential interaction forces by 76.8% and improve comfort. Finally, the control and delicate grasping assistance experiments validate the exoskeleton’s ability to realize the delicate grasping of the thumb. These characteristics show that the thumb exoskeleton has the potential to assist delicate thumb rehabilitation.

Development of an Untethered Adaptive Thumb Exoskeleton for Delicate Rehabilitation Assistance

Soft pressure sensors have recently attracted considerable attention because of their applications in human-machine interface, soft robotics, and prosthetics. However, there remain some challenges in achieving satisfactory performance (e.g., high sensitivity, wide sensing range, high stability) for soft pressure sensors. This article reports an intentional blocking based photoelectric pressure sensor. Two different blocking methods are investigated: the single-row-pyramid blocking and the double-row-pyramid blocking. The sensor has a simple structure, which is made of a light-emitting diode, photosensitive element, and silicone sensor shell. Experiments demonstrate that the sensor has a high sensitivity (the maximum sensitivity is 48.07 kPa-1, and the minimum measurement pressure is 0.8 Pa), large pressure-sensing range (the sensing range is up to 120 kPa), superior stability (a drift about 0.4% over 12,130 repetitive cycles at 0-80 kPa), low drift (< ±0.2% in different 3-day testing), negligible hysteresis, and high signal-to-noise ratio (over 55 dB). By mounting the pressure sensor at the end of a robotic arm, the robot can detect subtle collisions (such as touching a balloon through a pinpoint). In addition, this article fabricates a tactile glove based on the proposed pressure sensor and shows the application of this glove for music playing and object weighing. This study provides a new structure for photoelectric sensors to increase sensitivity and also provides a more convenient way to fabricate photoelectric pressure sensors.

Intentional Blocking Based Photoelectric Soft Pressure Sensor with High Sensitivity and Stability.

Various cognitive systems have been designed to model the position and stiffness profiles of human behavior and then to drive robots by mimicking the human’s behavior to accomplish physical human-robot interaction tasks through a properly designed impedance controller. However, some studies have shown that variable stiffness parameters of the impedance controller can cause the violation of the passivity constraint of the robot states, and make the robot’s stored energy exceed the external energy injected from the human user, thus leading to the unsafe human-robot interaction. To solve this problem, this paper proposes a novel passive model predictive impedance control method including two control loops. In the bottom-loop of the proposed controller, the robot is driven by a variable impedance controller to achieve the desired compliant interaction behavior. In the top-loop of the proposed controller, the model predictive control (MPC) is used to ensure that the robot states satisfy the passivity constraint by calculating a complementary torque to limit the stored energy of the robot. The passivity of the closed-loop robot system and the feasibility of MPC are guaranteed by theoretical analysis, ensuring the safety of the robotic movement in the human-robot interaction. The effectiveness of the proposed method is demonstrated by the simulation and experiment on the Franka Emika Panda robot. 

Passive Model Predictive Impedance Control for Safe Physical Human-Robot Interaction

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