Assessment of Jerk As a Method of Physical Fatigue Detection

Author(s):  
Lichen Zhang ◽  
Mohsen Mutasem Diraneyya ◽  
JuHyeong Ryu ◽  
Carl T. Haas ◽  
Eihab Abdel-Rahman

Workers’ fatigue is a significant problem in physically demanding occupations. Physical fatigue is known to result in the inability to maintain proper posture and working technique. Consequently, workers lose their ability to safely and effectively perform their duties. Thus, understanding the physical demands of labor-intensive work is of great importance in protecting workers’ safety, and maintaining productivity. Current fatigue assessments methods, including surveys and questionnaires, are subjective and lack reliability. Objective fatigue assessments based on physiological data are more reliable, however they are cumbersome to implement in real work conditions. There is a need for an objective fatigue assessment method that can monitor physical fatigue with minimal intrusion. The goal of this study was to investigate whether jerk, the time-derivative of acceleration, can be used to objectively detect physical fatigue. A pilot study on masons was conducted to determine if physical fatigue can be detected by changes in jerk values. Ten participants performed a bricklaying task using forty-five concrete masonry units (CMU). Seven body segments, namely the hands, forearms, upper arms, and pelvis, were selected for placement of IMU sensors to measure the segment accelerations. Jerk was calculated from the measured acceleration via numerical differentiation. Characteristic values of the jerk at the beginning and end of the bricklaying task were obtained to represent the rested and fatigued states. They were then compared for significant differences. Jerk values calculated from the IMU sensors located on the upper arms and pelvis showed significant differences between rested and fatigued states. The results of this pilot study indicate that the characteristic jerk can be used to detect physical fatigue, however caution must be taken in selecting sensor locations to reduce the influence of spurious signals.

2011 ◽  
Vol 20 (4) ◽  
pp. 325-336 ◽  
Author(s):  
Mattias Wallergård ◽  
Peter Jönsson ◽  
Gerd Johansson ◽  
Björn Karlson

One of the most common methods of inducing stress in the laboratory in order to examine the stress response in healthy and clinical populations is the Trier Social Stress Test (TSST). Briefly, the participant is asked to deliver a speech and to perform an arithmetic task in front of an evaluating committee. The committee, consisting of three trained actors, does not respond emotionally during the test, which makes the situation very stressful for the participant. One disadvantage of the TSST is that it can be difficult to hold the experimental conditions constant. In particular, it may be difficult for actors to hold their acting constant across all sessions. Furthermore, there are several practical problems and costs associated with hiring professional actors. A computerized version of the TSST using virtual humans could be a way to avoid these problems provided that it is able to induce a stress response similar to the one of the original TSST. The purpose of the present pilot study was therefore to investigate the stress response to a virtual reality (VR) version of the TSST visualized using an immersive VR system (VR-TSST). Seven healthy males with an average age of 24 years (range: 23–26 years) performed the VR-TSST. This included delivering a speech and performing an arithmetic task in front of an evaluating committee consisting of three virtual humans. The VR equipment was a CAVE equipped with stereoscopy and head tracking. ECG and respiration were recorded as well as the participant's behavior and comments. Afterward, a semi-structured interview was carried out. In general, the subjective and physiological data from the experiment indicated that the VR version of the TSST induced a stress response in the seven participants. In particular, the peak increase in heart rate was close to rates observed in studies using the traditional TSST with real actors. These results suggest that virtual humans visualized with an immersive VR system can be used to induce stress under laboratory conditions.


2014 ◽  
Vol 26 (5) ◽  
pp. 321-324
Author(s):  
Sónia Martins ◽  
Patrícia Moldes ◽  
João Pinto-de-Sousa ◽  
Filipe Conceição ◽  
José Artur Paiva ◽  
...  

ObjectiveTo present the pilot study on the European Portuguese validation of the Confusion Assessment Method (CAM).MethodsThe translation process was carried out according to International Society Pharmacoeconomics and Outcomes Research guidelines with trained researchers and inter-rater reliability assessment. The study included 50 elderly patients, admitted (≥24 h) to two intermediate care units. Exclusion criteria were: Glasgow Coma Scale (total score ≤11), blindness/deafness, inability to communicate and not able to speak Portuguese. The sensitivity and specificity of CAM were assessed, with DSM-IV-TR criteria of delirium used as a reference standard.ResultsFindings revealed excellent inter-rater reliability (k>0.81), moderate sensitivity (73%) and excellent specificity (95%).ConclusionThese preliminary results suggested that this version emerges as a promising diagnostic instrument for delirium.


2020 ◽  
Vol 120 ◽  
pp. 103381
Author(s):  
Xuejiao Xing ◽  
Botao Zhong ◽  
Hanbin Luo ◽  
Timothy Rose ◽  
Jue Li ◽  
...  

2020 ◽  
Vol 12 (7) ◽  
pp. 2714
Author(s):  
Farnad Nasirzadeh ◽  
Mostafa Mir ◽  
Sadiq Hussain ◽  
Mohammad Tayarani Darbandy ◽  
Abbas Khosravi ◽  
...  

Physical fatigue is one of the most important and highly prevalent occupational hazards in different industries. This research adopts a new analytical framework to detect workers’ physical fatigue using heart rate measurements. First, desired features are extracted from the heart signals using different entropies and statistical measures. Then, a feature selection method is used to rank features according to their role in classification. Finally, using some of the frequently used classification algorithms, physical fatigue is detected. The experimental results show that the proposed method has excellent performance in recognizing the physical fatigue. The achieved accuracy, sensitivity, and specificity rates for fatigue detection are 90.36%, 82.26%, and 96.2%, respectively. The proposed method provides an efficient tool for accurate and real-time monitoring of physical fatigue and aids to enhance workers’ safety and prevent accidents. It can be useful to develop warning systems against high levels of physical fatigue and design better resting times to improve workers’ safety. This research ultimately aids to improve social sustainability through minimizing work accidents and injuries arising from fatigue.


2020 ◽  
Vol 23 (4) ◽  
pp. 277-282
Author(s):  
SA. De Freitas ◽  
EKC. Wong ◽  
JY. Lee ◽  
C. Reppas-Rindlisbacher ◽  
C. Gabor ◽  
...  

Background Delirium is characterized by fluctuating attention or arousal, with high prevalence in the orthopaedic ward. Our aim was to: 1) establish the prevalence of delirium on an orthopaedic ward, and 2) compare delirium prevalence using a single geriatrician assessment vs. multiple 3D-CAM (3-Minute Diagnostic Interview for Confusion Assessment Method) assessments during the day. We hypothesized that multiple assessments would increase the detection rate due to the fluctuating nature of delirium. Methods Comparative study conducted at an academic hospital in Hamilton, Ontario. Participants included patients 65 years and older admitted to the orthopaedic ward (n=55). After a geriatrician made the first assessment of delirium by 3D-CAM on each patient, teams with specialized geriatrics training re-assessed participants up to four times. Delirium rates based on first assessment were compared to cumulative end-of-day rates to determine if detection increased with multiple assessments. Results The prevalence of delirium was 30.9% (17 participants) us­ing multiple assessments. Of these cases, 13 (76.4%) were detected in the initial geriatrician assessment. In patients with hip fractures, 70.6% (12 of 17) were identified as delirious by multiple assessments. Conclusion As symptoms fluctuate, multiple daily CAM assessments may increase the identification of delirium in orthopaedic inpatients.


2017 ◽  
Vol 7 (1.1) ◽  
pp. 125
Author(s):  
S. Jeyalaksshmi ◽  
S. Prasanna

In real life scenario, facial expressions and emotions are nothing but responses to the external and internal events of human being. In Human Computer Interaction (HCI), recognition of end user’s expressions and emotions from the video streaming plays very important role. In such systems it is required to track the dynamic changes in human face movements quickly in order to deliver the required response system. In real time applications, this Facial Expression Recognition (FER) is very helpful like physical fatigue detection based on facial detection and expressions such as driver fatigue detection in order to prevent the accidents on road. Face expression based physical fatigue analysis or detection is out of scope of this work, but this work proposed a Simultaneous Evolutionary Neural Network (SENN) classification scheme is proposed for recognising human emotion or expression. In this work, at first, automatically detects and tracks facial landmarks in videos, and face is detected by using enhanced adaboost algorithm with haar features. Then, in order to describe facial expression modifications, geometric features are taken out and the Local Binary Pattern (LBP) is extracted to improve the detection accuracy and it has a much lower-dimensional size. With the aim of examining the temporal facial expression modifications, we apply SENN probabilistic classifiers, which examine the facial expressions in individual frames, and after that promulgate the likelihoods during the course of the video to take the temporal features of facial expressions such as glad, sad, anger, and fear feelings. The experimental results show that the performance of proposed SENN scheme is attained better results compared than existing recognition schemes like Time-Delay Neural Network with Support Vector Regression (TDNN-SVR) and SVR. 


2019 ◽  
Vol 103 ◽  
pp. 1-12 ◽  
Author(s):  
Yantao Yu ◽  
Heng Li ◽  
Xincong Yang ◽  
Liulin Kong ◽  
Xiaochun Luo ◽  
...  

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