Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis

Human fatigue due to repetitive and physically challenging jobs may result in poor performance and a Work-related Musculoskeletal Disorder (WMSD). Thus, the importance of being able to monitor fatigue to implement preventative interventions cannot be overstated. This study was designed to monitor fatigue through the development of a methodology that objectively classifies an individual’s level of fatigue in the workplace by utilizing the motion sensors embedded in smartphones. An experiment consisting of squatting tasks, primarily involving the lower extremity musculature, was conducted with 24 participants using a smartphone attached to their right shank. Using Borg’s Ratings of Perceived Exertion (RPE) to label gait data, we developed machine learning algorithms to classify each individual’s gait into two (no- vs. strong-fatigue), three (no-, medium-, and strong-fatigue) and four (no-, low-, medium-, and strong-fatigue) levels of fatigue, for which accuracy of 91%, 76%, and 61% were obtained, respectively. The outcomes of this study may facilitate the implementation of a proactive approach supporting the continuous monitoring of a worker’s fatigue level, which may subsequently enhance workers’ performance and reduce the risk of WMSDs

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

Time–frequency Analysis of the EMG Digital Signals

In the article comparison of time-frequency spectra of EMG signals obtained by the following methods: Fast Fourier Transform, predictive analysis and wavelet analysis is presented. The EMG spectra of biceps and triceps while an adult man was flexing his arm were analysed. The advantages of the predictive analysis were shown as far as averaging of the spectra and determining the main maxima are concerned. The Continuous Wavelet Transform method was applied, which allows for the proper distribution of the scales, aiming at an accurate analysis and localisation of frequency maxima as well as the identification of impulses which are characteristic of such signals (bursts) in the scale of time. The modified Morlet wavelet was suggested as the mother wavelet. The wavelet analysis allows for the examination of the changes in the frequency spectrum in particular stages of the muscle contraction. Predictive analysis may also be very useful while smoothing and averaging the EMG signal spectrum in time

Aerospace Medicine and Biology: A continuing bibliography, supplement 216

One hundred twenty reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1981 are listed. Topics include: sanitary problems; pharmacology; toxicology; safety and survival; life support systems; exobiology; and personnel factors

Biophysical Modulations of Functional Connectivity

Resting-state low frequency oscillations have been detected in many functional magnetic resonance imaging (MRI) studies and appear to be synchronized between functionally related areas. Converging evidence from MRI and other imaging modalities suggest that this activity has an intrinsic neuronal origin. Multiple consistent networks have been found in large populations, and have been shown to be stable over time. Further, these patterns of functional connectivity have been shown to be altered in healthy controls under various physiological challenges. This review will present the biophysical characterization of functional connectivity, and examine the effects of physical state manipulations (such as anesthesia, fatigue, and aging) in healthy controls.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90432/1/brain-2E2011-2E0039.pd

detection of onset muscle fatigue based on joint analysis of surface electromyography spectrum and amplitude

Many studies have been conducted to track muscle fatigue and to understand the mechanisms that contribute to the deterioration of muscle performance. Electromyography fatigue threshold (EMGFT) and Integrated Electromyography (IEMG) are two techniques that have been applied to determine the Onset of Muscle Fatigue (OMF) by depending on the percentage force output and amplitude respectively. Nevertheless, force and amplitude are correlated with one another during fatigue. Joint Analysis of EMG Spectrum and Amplitude (JASA) is commonly used to discriminate force-related from fatigue induced EMG changes. However, the length of signal affects the performance of JASA in discriminating fatigue signal. Apart from that, JASA has not been used to detect OMF. Thus, the purpose of this study is to determine the OMF region by applying JASA on the segmented EMG signal. Surface EMG signals were recorded from 30 college students while they were performing isometric contractions of Biceps Brachii muscles for 2 minutes. Each recorded signal was segmented into 15-second time interval. Root Mean Square (RMS) and Mean Frequency (MNF) were used as the muscle fatigue indicators. The indicators were extracted from 3-second epoch length within each segment. A polynomial regression model was applied to describe the trends of the indicators in a segment. The first segment that simultaneously showed a decrease in the frequency and an increase in the amplitude of a sEMG signal with correlation coefficient r ≥ 0.7 was classified as the region where the OMF occurred. Out of 30 subjects, 20 subjects (67%) either admitted to experience muscle discomfort and at the same time the OMF region was also detected or vice-versa. For the other 10 subjects, the OMF region was able to be detected in 90% of them but due to better endurance levels, they required longer time to experience muscle discomfort. The temporal-spectral fatigue indicator (Instantaneous Mean Frequency (iMNF)) was used to determine the reliability of the developed technique. The decrement of iMNF on the detected OMF region showed high correlation coefficient (r > 0.6). The subjects were also asked to perform dynamic contractions for 2 minutes. The proposed technique was applied to the recorded signals and the OMF was detected in 24 subjects. Eighteen of them (72%) acknowledged that they had experienced muscle discomfort. Fourteen out of 18 subjects felt muscle discomfort after OMF was detected. The results indicate that muscle discomfort develops gradually after the onset of muscle fatigue. For handwriting activity, 4 subjects were asked to write for 5 minutes while the sEMG signals were captured from Flexor Carpi Radialis muscle (small muscle). Out of 4 subjects, all of them showed an increment in pen pressure, and 75% of them showed an increment in the writing speed after detecting OMF region. This study concludes that the proposed technique is feasible to detect the OMF; not only during isometric contraction but also during dynamic contraction. The technique also has the potential to be applied to small muscle contraction

Differentiating Variations in Thumb Position From Recordings of the Surface Electromyogram in Adults Performing Static Grips, a Proof of Concept Study

Hand gesture and grip formations are produced by the muscle synergies arising between extrinsic and intrinsic hand muscles and many functional hand movements involve repositioning of the thumb relative to other digits. In this study we explored whether changes in thumb posture in able-body volunteers can be identified and classified from the modulation of forearm muscle surface electromyography (sEMG) alone without reference to activity from the intrinsic musculature. In this proof-of-concept study, our goal was to determine if there is scope to develop prosthetic hand control systems that may incorporate myoelectric thumb-position control. Healthy volunteers performed a controlled-isometric grip task with their thumb held in four different opposing-postures. Grip force during task performance was maintained at 30% maximal-voluntary force and sEMG signals from the forearm were recorded using 2D high-density sEMG (HD-sEMG arrays). Correlations between sEMG amplitude and root-mean squared estimates with variation in thumb-position were investigated using principal-component analysis and self-organizing feature maps. Results demonstrate that forearm muscle sEMG patterns possess classifiable parameters that correlate with variations in static thumb position (accuracy of 88.25±0.5% anterior; 91.25±2.5% posterior musculature of the forearm sites). Of importance, this suggests that in transradial amputees, despite the loss of access to the intrinsic muscles that control thumb action, an acceptable level of control over a thumb component within myoelectric devices may be achievable. Accordingly, further work exploring the potential to provide myoelectric control over the thumb within a prosthetic hand is warranted

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 324)

This bibliography lists 200 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during May, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance