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IEEE/ASME International conference on advanced Intelligent Mechatronics (AIM)

作者： Zhu, Chunchu Maurya, Sushant Yi, Jingang Dutta, Ashish Rutgers State Univ Dept Mech & Aerosp Engn Piscataway NJ 08854 USA Indian Inst Technol Dept Mech Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9798350355376;9798350355369

As the aging and disabled populations grow, the demand for effective assistance in daily activities such as sit-to-stand (STS) has been increasing. Wearable exoskeletons are a promising technology for reducing the effort required for STS. This study explores the sensor fusion of electroencephalography (EEG) signals, which reveal pre-movement intentions, with inertial measurement unit (IMU) data, offering real-time motion information for enhanced knee exoskeleton control in STS assistance. The EEG-IMU sensor fusion approach improves the temporal accuracy and robustness of STS intention detection. By detecting STS intentions with lower latencies, the knee exoskeleton provides timely and smooth support, enhancing the user experience. The proposed method reduces system latency, enabling rapid user-exoskeleton interaction. Experimental results demonstrate the effectiveness of the brain-computer interface (BCI)-enhanced knee exoskeleton for improving STS efficiency and user experience.

关键词： Exoskeleton (Robotics)

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Cameraless sensor fusion: developing a cost-effective driver assistance system using radar and ultrasonic sensor

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sensor REVIEW 2025年第2期45卷 186-197页

作者： Sasikumar, S. Balaji, B. Aravind Joshuva, A. Deivanayagampillai, Nagarajan Hindustan Inst Technol & Sci Dept Elect & Commun Chennai India Dayananda Sagar Univ Sch Engn Dept Comp Sci & Engn AI&ML Bangalore India Rajalakshmi Inst Technol Dept Math Chennai India

PurposeThis paper aims to develop a cost-effective, camera-less advanced driver assistance system (ADAS) for electric vehicles. It will use sensor fusion of ultrasonic and radar sensors to implement adaptive cruise control (ACC), blind spot detection (BSD) and reverse parking (RP).Design/methodology/approachThe system was tested on an electric vehicle test bench, using strategically placed ultrasonic and radar sensors. sensor fusion enabled accurate object detection and distance measurement. The system's performance was evaluated through simulated obstacle scenarios, with responses monitored via a graphical user interface. sensor and GPS data were transmitted to the cloud for potential vehicle-to-vehicle *** sensor fusion approach effectively supported ACC, BSD and RP functions, demonstrating accuracy in obstacle detection, speed adjustment and emergency braking. The real-time system visualization confirmed reliability across various scenarios and cloud integration showed promise for future communication *** limitations/implicationsUltrasonic and radar sensors have limited range and accuracy compared to cameras. Ultrasonic sensors are less effective at longer distances and in adverse weather conditions, whereas radar can face challenges in detecting small or stationary objects. sensor performance can be affected by environmental factors such as rain, fog or snow, which may reduce the effectiveness of both ultrasonic and radar sensors. sensor performance can be affected by environmental factors such as rain, fog or snow, which may reduce the effectiveness of both ultrasonic and radar *** implicationsImproved obstacle detection and collision avoidance contribute to overall vehicle safety. Drivers benefit from advanced features like ACC, BSD and RP without the high cost of traditional camera-based systems. The use of ultrasonic and radar sensors makes advanced driver assistance features more affordable, allowing bro

关键词： sensor fusion Adaptive driver assistance system (ADAS) V2V Communication

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sensory Fiber-Based Electronic Device as Intelligent and Natural User interface

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advanced FIBER MATERIALS 2025年第3期7卷 827-840页

作者： Wei, Xiao Xiang, Shengxin Meng, Chongguang Chen, Zhishui Cao, Shuze Hong, Jianlong Duan, Shengshun Liu, Lei Zhang, Huiyun Shi, Qiongfeng Shen, Guozhen Wu, Jun Southeast Univ Sch Elect Sci & Engn Joint Int Res Lab Informat Display & Visualizat Nanjing 210096 Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China

A natural user interface (NUI) with ample information perception capability is a crucial element for the next-generation human-machine interaction and the development of the intelligent era. However, significant challenges remain to be solved in developing intelligent and natural interfaces with satisfactory smart sensing performance. Here, we report an NUI based on an intelligent fabric bracelet empowered with wide-range pressure detectability, enabling invisible and efficient human-machine interaction. The wide-range pressure-sensing ability of the fiber-based pressure sensor can be attributed to the coupling mechanism of contact resistance change and quantum tunneling effect. The fiber-based sensor array is then integrated with a miniaturized wireless flexible printed circuit board, forming an intelligent and compact fabric bracelet system for natural interactive applications in wireless smart home control and virtual reality. It is envisioned that the NUI based on the pressure-sensitive and intelligent fabric bracelet will significantly contribute to the development of next-generation NUIs for more diversified control and interactive applications.

关键词： Natural user interface Human-machine interaction Pressure sensor Fiber sensor Wearable electronics

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Force, Humidity, and Temperature Estimation of a Multi-modal Soft Actuator for Human-Pad interface

Force, Humidity, and Temperature Estimation of a Multi-modal...

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IEEE/ASME International conference on advanced Intelligent Mechatronics (AIM)

作者： Twomey, Pat Varma, Vaibhaysingh Trkov, Mitja Rowan Univ Dept Mech Engn Glassboro NJ 08028 USA

ISBN: (纸本)9798350355376;9798350355369

Pressure injuries in long-term care facilities present a significant problem for the well-being of bedridden patients and the overall cost of the healthcare systems. Mitigating risks of pressure injury formation might be possible through monitoring and control of the main extrinsic factors that cause them, including temperature, humidity, and normal and shear loads at the skin-support surface interface. An instrumented soft robotic pad system serving as a support surface is a potential solution. In this work, we present the design of two-degree-of-freedom soft actuators that when combined in a grid form an instrumented soft pad. The actuators have integrated humidity sensor, thermistor, and embedded force sensitive resistor (FSR). We investigate the optimal placement of the embedded sensors to monitor temperature, humidity, and applied normal loads during various actuation modes. We utilize a long short-term memory (LSTM) neural network to obtain estimated values of humidity and temperature at the expected contact interface, and also estimates of the normal loads exerted on the soft actuators under various actuation configurations that affect raw FSR sensor measurements. The developed system can be potentially used to monitor and mitigate pressure injuries risks factors in long-term care patients and enhance the quality of care of those patients.

关键词： Shear stress

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Digital Sensing systems for Electromyography

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IEEE TRANSACTIONS ON NEURAL systemS AND REHABILITATION ENGINEERING 2024年 32卷 2826-2834页

作者： Aghchehli, Eisa Kyranou, Iris Dyson, Matthew Nazarpour, Kianoush Newcastle Univ Sch Engn Newcastle Upon Tyne NE1 7RU England Univ Edinburgh Sch Informat Edinburgh EH8 9AB Scotland

Surface electromyogram (EMG) signals find diverse applications in movement rehabilitation and human-computer interfacing. For instance, future advanced prostheses, which use artificial intelligence, will require EMG signals recorded from several sites on the forearm. This requirement will entail complex wiring and data handling. We present the design and evaluation of a bespoke EMG sensing system that addresses the above challenges, enables distributed signal processing, and balances local versus global power consumption. Additionally, the proposed EMG system enables the recording and simultaneous analysis of skin-sensor impedance, needed to ensure signal fidelity. We evaluated the proposed sensing system in three experiments, namely, monitoring muscle fatigue, real-time skin-sensor impedance measurement, and control of a myoelectric computer interface. The proposed system offers comparable signal acquisition characteristics to that achieved by a clinically-approved product. It will serve and integrate future myoelectric technology better via enabling distributed machine learning and improving the signal transmission efficiency.

关键词： Electromyography sensors Electrodes Prosthetics sensor systems Impedance Skin Digital electromyography sensors-skin impedance

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A TENG Finger sensor Enabled Human-Machine interface for VR Applications

A TENG Finger Sensor Enabled Human-Machine Interface for VR ...

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2024 IEEE Region 10 conference, TENCON 2024

作者： Wu, Chuyang Sun, Zhongda School of Electrical and Information Engineering Tianjin University Tianjin China National University of Singapore The Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350350821

A wireless, triboelectric nanogenerator (TENG) sensor enabled handle-like human-machine interface (HMI) is proposed to capture finger motions and manipulate game objects in virtual reality. By utilizing advanced signal processing methods including logarithmic transform and integration, peak-like TENG signals are transformed into stage-like signals to achieve continuous control. Haptic feedback supported by vibrators can bring better game experiences to players. Finally, a gesture recognition system based on processed sensor signals is also developed to categorize 10 common gestures. © 2024 IEEE.

关键词： Triboelectricity

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Enabling feedback position control of an industrial robot based on external sensor signals for dual-stage actuation

Enabling feedback position control of an industrial robot ba...

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IEEE/ASME International conference on advanced Intelligent Mechatronics (AIM)

作者： Laimer, Matthias Wertjanz, Daniel Gsellmann, Peter Schitter, Georg Csencsics, Ernst Tech Univ Wien Automat & Control Inst Vienna Austria Tech Univ Wien Automat & Control Inst ACIN Christian Doppler Lab Precis Measurements Mot A-1040 Vienna Austria

ISBN: (纸本)9798350355376;9798350355369

This paper presents an advanced control error processing to enable the feedback position control of an industrial robot based on external sensor signals for dual-stage actuation. A measurement module comprising a magnetically levitated platform with an integrated 3D measurement tool is mounted as end effector on the industrial robot. The industrial robot is used to maintain the measurement platform within its limited actuation range, while actively tracking a sample in motion. The measurement module provides the short stroke but precision positioning of the measurement tool relative to a sample surface in 6-DoF. For a feedback controlled repositioning of the industrial robot's tool center point based on the internal measurement platform's position signals, a real-time interface is used. The implementation of the advanced control error processing successfully demonstrates the active tracking of a sample in motion, with residual tracking errors of 473 nm rms in sample-motion direction.

关键词： Mechatronics inline tracking systems dual stage-actuation

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An Integrated and Flexible Humidity sensor for Noncontact Handwriting Recognition

ADVANCED INTELLIGENT SYSTEMS

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advanced INTELLIGENT systemS 2025年第0期

作者： Xiao, Yu Yao, Jingjing Zhu, Shujuan Minjiang Univ Coll Phys & Elect Informat Engn Fuzhou 350108 Peoples R China Chinese Acad Sci Inst Semicond Beijing 100089 Peoples R China

The demand for human-machine interfaces (HMIs) is soaring with the rapid development of wearable electronics. However, current HMIs typically operate in contact sensing mode, which can cause interface contamination, and require the combination of rigid sampling circuits. Therefore, revolutionary integrated noncontact flexible HMI systems are eagerly anticipated. In this work, a 4 x 4 flexible humidity sensing array and a designed flexible sampling circuit are directly integrated to form a noncontact handwriting recognition system. The humidity sensing array is fabricated by screen printing the interdigital electrode array on PET substrate with MXene acting as the humidity-sensitive film. The sensor works in noncontact mode in response to human respiration and fingertip humidity fields. The flexible sampling circuit allows simultaneous acquisition of 16 port signals and transmission to the computer. Combined with a convolutional neural network, the average accuracy of the noncontact handwriting recognition system for recognizing handwriting digits 1-9 is achieved to be highly 98.67%. The noncontact handwriting recognition system is further applied to control a mechanical hand to visualize handwriting digits in real time. This noncontact handwriting recognition system expands a new path for humidity sensors and shows an exceptional application prospect in the next generation of HMI and touchless medical equipment.

关键词： deep learning flexible sensing systems humidity sensors noncontact human-machine interfaces

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Conception of a system-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation

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APPLIED SCIENCES-BASEL 2025年第4期15卷 1699-1699页

作者： Nieto, Ruben Fernandez, Pedro R. Murano, Santiago Navarro, Victor M. del-Ama, Antonio J. Borromeo, Susana Rey Juan Carlos Univ Elect Technol Area Mostoles 28933 Spain Univ Alcala Elect Dept Alcala De Henares 28805 Spain

Electromyography (EMG) signals are fundamental in neurorehabilitation as they provide a non-invasive means of capturing the electrical activity of muscles, enabling precise detection of motor intentions. This capability is essential for controlling assistive devices, such as therapeutic exoskeletons, that aim to restore mobility and improve motor function in patients with neuromuscular impairments. The integration of EMG into neurorehabilitation systems allows for adaptive and patient-specific interventions, addressing the variability in motor recovery needs. However, achieving the high fidelity, low latency, and robustness required for real-time control of these devices remains a significant challenge. This paper introduces a novel multi-channel electromyography (EMG) acquisition system implemented on a system-on-Chip (SoC) architecture for robotic neurorehabilitation. The system employs the Zynq-7000 SoC, which integrates an advanced RISC Machine (ARM) processor, for high-level control and an FPGA for real-time signal processing. The architecture enables precise synchronization of up to eight EMG channels, leveraging high-speed analog-to-digital conversion and advanced filtering techniques implemented directly at the measurement site. By performing filtering and initial signal processing locally, prior to transmission to other subsystems, the system minimizes noise both through optimized processing and by reducing the distance to the muscle, thereby significantly enhancing the signal-to-noise ratio (SNR). A dedicated communication interface ensures low-latency data transfer to external controllers, crucial for adaptive control loops in exoskeletal applications. Experimental results validate the system's capability to deliver high signal fidelity and low processing delays, outperforming commercial alternatives in terms of flexibility and scalability. This implementation provides a robust foundation for real-time bio-signal processing, advancing the integration of E

关键词： SoC architectures neurorehabilitation EMG rehabilitation robotics human-machine interaction sensor integration

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Enhancing Heavy Vehicle Safety with Intelligent Driver Behaviour Monitoring via Multi-sensor Fusion and Embedded systems

Enhancing Heavy Vehicle Safety with Intelligent Driver Behav...

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2024 International conference on system, Computation, Automation and Networking, ICSCAN 2024

作者： Kanagamalliga, S. Nath, Srigitha S Nivetha, T. Baby Shalini, M. Saveetha Engineering College Dept. of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331510022

This research presents the development of a sensor fusion-based driver assistance system (DAS) aimed at enhancing the safety of heavy vehicle operators. The system integrates an alcohol sensor, an ADXL 335 accelerometer sensor, and an eye blink detection sensor with an Arduino platform for real-time monitoring of driver behaviour. LabVIEW software was employed for data processing and visualization, providing a user-friendly interface to track driver alertness, speed, and alcohol consumption. A classification algorithm was implemented to categorize the driver's condition into various risk levels based on sensor inputs. Alerts were generated in real-time to notify the driver of potential hazards, such as drowsiness, intoxication, or speeding. Experimental trials demonstrated the system's effectiveness in accurately detecting dangerous driving behaviours and issuing timely warnings. The use of sensor fusion enhanced data reliability by cross-verifying multiple inputs, reducing false positives. The system proved to be robust and practical for implementation in heavy vehicle operations, contributing to road safety. Future enhancements may include additional physiological sensors, integration with vehicle control systems, and the practise of machine learning (ML) algorithms to improve classification accuracy. This research highlights the potential of sensor fusion technology in the development of advanced DAS, particularly in the transportation sector. © 2024 IEEE.

关键词： advanced driver assistance systems

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