A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Current state of digital signal processing in myoelectric interfaces and related applications

This review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented.Peer reviewe

Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations

Systems engineering approaches to safety in transport systems

openDuring driving, driver behavior monitoring may provide useful information to prevent road traffic accidents caused by driver distraction. It has been shown that 90% of road traffic accidents are due to human error and in 75% of these cases human error is the only cause. Car manufacturers have been interested in driver monitoring research for several years, aiming to enhance the general knowledge of driver behavior and to evaluate the functional state as it may drastically influence driving safety by distraction, fatigue, mental workload and attention. Fatigue and sleepiness at the wheel are well known risk factors for traffic accidents. The Human Factor (HF) plays a fundamental role in modern transport systems. Drivers and transport operators control a vehicle towards its destination in according to their own sense, physical condition, experience and ability, and safety strongly relies on the HF which has to take the right decisions. On the other hand, we are experiencing a gradual shift towards increasingly autonomous vehicles where HF still constitutes an important component, but may in fact become the "weakest link of the chain", requiring strong and effective training feedback. The studies that investigate the possibility to use biometrical or biophysical signals as data sources to evaluate the interaction between human brain activity and an electronic machine relate to the Human Machine Interface (HMI) framework. The HMI can acquire human signals to analyse the specific embedded structures and recognize the behavior of the subject during his/her interaction with the machine or with virtual interfaces as PCs or other communication systems. Based on my previous experience related to planning and monitoring of hazardous material transport, this work aims to create control models focused on driver behavior and changes of his/her physiological parameters. Three case studies have been considered using the interaction between an EEG system and external device, such as driving simulators or electronical components. A case study relates to the detection of the driver's behavior during a test driver. Another case study relates to the detection of driver's arm movements according to the data from the EEG during a driver test. The third case is the setting up of a Brain Computer Interface (BCI) model able to detect head movements in human participants by EEG signal and to control an electronic component according to the electrical brain activity due to head turning movements. Some videos showing the experimental results are available at https://www.youtube.com/channel/UCj55jjBwMTptBd2wcQMT2tg.openXXXIV CICLO - INFORMATICA E INGEGNERIA DEI SISTEMI/ COMPUTER SCIENCE AND SYSTEMS ENGINEERING - Ingegneria dei sistemiZero, Enric

Towards electrodeless EMG linear envelope signal recording for myo-activated prostheses control

After amputation, the residual muscles of the limb may function in a normal way, enabling the electromyogram (EMG) signals recorded from them to be used to drive a replacement limb. These replacement limbs are called myoelectric prosthesis. The prostheses that use EMG have always been the first choice for both clinicians and engineers. Unfortunately, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.); researchers have aspired to find suitable alternatives. One proposes the dry-contact, low-cost sensor based on a force-sensitive resistor (FSR) as a valid alternative which instead of detecting electrical events, detects mechanical events of muscle. FSR sensor is placed on the skin through a hard, circular base to sense the muscle contraction and to acquire the signal. Similarly, to reduce the output drift (resistance) caused by FSR edges (creep) and to maintain the FSR sensitivity over a wide input force range, signal conditioning (Voltage output proportional to force) is implemented. This FSR signal acquired using FSR sensor can be used directly to replace the EMG linear envelope (an important control signal in prosthetics applications). To find the best FSR position(s) to replace a single EMG lead, the simultaneous recording of EMG and FSR output is performed. Three FSRs are placed directly over the EMG electrodes, in the middle of the targeted muscle and then the individual (FSR1, FSR2 and FSR3) and combination of FSR (e.g. FSR1+FSR2, FSR2-FSR3) is evaluated. The experiment is performed on a small sample of five volunteer subjects. The result shows a high correlation (up to 0.94) between FSR output and EMG linear envelope. Consequently, the usage of the best FSR sensor position shows the ability of electrode less FSR-LE to proportionally control the prosthesis (3-D claw). Furthermore, FSR can be used to develop a universal programmable muscle signal sensor that can be suitable to control the myo-activated prosthesis

Analysis of gamma-band activity from human EEG using empirical mode decomposition

The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD decomposes the original signal into a finite number of intrinsic mode functions (IMFs). EEGs from 25 control subjects were registered in basal and motor activity (hand movements) using only one EEG channel. Over the basic signal, IMF signals are computed. Gamma-band activity is computed using power spectrum density in the 30–60 Hz range. Event-related synchronization (ERS) was defined as the ratio of motor and basal activity. To evaluate the performance of the new EMD based method, ERS was computed from the basic and IMF signals. The ERS obtained using IMFs improves, from 31.00% to 73.86%, on the original ERS for the right hand, and from 22.17% to 47.69% for the left hand. As EEG processing is improved, the clinical applications of gamma-band activity will expand.Universidad de AlcaláInstituto de Salud Carlos II