A Subject-Specific Approach to Detect Fatigue-Related Changes in Spine Motion Using Wearable Sensors

Wearables offer a promising solution for simultaneous posture monitoring and/or corrective feedback. The main objective was to identify, synthesise, and characterise the wearables used in the workplace to monitor and postural feedback to workers. The PRISMA-ScR guidelines were followed. Studies were included between 1 January 2000 and 22 March 2023 in Spanish, French, English, and Portuguese without geographical restriction. The databases selected for the research were PubMed®, Web of Science®, Scopus®, and Google Scholar®. Qualitative studies, theses, reviews, and meta-analyses were excluded. Twelve studies were included, involving a total of 304 workers, mostly health professionals (n = 8). The remaining studies covered workers in the industry (n = 2), in the construction (n = 1), and welders (n = 1). For assessment purposes, most studies used one (n = 5) or two sensors (n = 5) characterised as accelerometers (n = 7), sixaxial (n = 2) or nonaxialinertial measurement units (n = 3). The most common source of feedback was the sensor itself (n = 6) or smartphones (n = 4). Haptic feedback was the most prevalent (n = 6), followed by auditory (n = 5) and visual (n = 3). Most studies employed prototype wearables emphasising kinematic variables of human movement. Healthcare professionals were the primary focus of the study along with haptic feedback that proved to be the most common and effective method for correcting posture during work activities.

Section: Discussionmentioning

confidence: 99%

Wearables for Monitoring and Postural Feedback in the Work Context: A Scoping Review

Figueira,

Silva,

Costa

et al. 2024

“…These results are in accordance with those found by Picerno et al [ 14 ], which also validated the Magneto-Inertial technology for vertical jump monitoring. In addition, MIMUs was also used by Chan et al to detect fatigue conditions by analysing ten kinematic variables of the spine [ 28 ] from two inertial units. Similarly to our study, they proposed an index based on the aforementioned ten kinematic variables (Spine Motion Composite Index-SMCI) for spine motion analysis after a fatigue protocol.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Biomechanical Response to Fatigue through Wearable Sensors in Semi-Professional Football Referees

Truppa

Guaitolini

Garofalo³

et al. 2020

Quantifying muscle fatigue is a key aspect of everyday sport practice. A reliable and objective solution that can fulfil this task would be deeply important for two main reasons: (i) it would grant an objective indicator to adjust the daily training load for each player and (ii) it would provide an innovative tool to reduce the risk of fatigue-related injuries. Available solutions for objectively quantifying the fatigue level of fatigue can be invasive for the athlete; they could alter the performance or they are not compatible with daily practice on the playground. Building on previous findings that identified fatigue-related parameters in the kinematic of the counter-movement jump (CMJ), this study evaluates the physical response to a fatigue protocol (i.e., Yo-Yo Intermittent Recovery Test Level 1) in 16 football referees, by monitoring CMJ performance with wearable magneto-inertial measurement units (MIMU). Nineteen kinematic parameters were selected as suitable indicators for fatigue detection. The analysis of their variations allowed us to distinguish two opposites but coherent responses to the fatigue protocol. Indeed, eight out of sixteen athletes showed reduced performance (e.g., an effective fatigue condition), while the other eight athletes experienced an improvement of the execution likely due to the so-called Post-Activation Potentiation. In both cases, the above parameters were significantly influenced by the fatigue protocol (p < 0.05), confirming their validity for fatigue monitoring. Interesting correlations between several kinematic parameters and muscular mass were highlighted in the fatigued group. Finally, a “fatigue approximation index” was proposed and validated as fatigue quantifier.

“…Inertial measurement units (IMUs) are reliable and cheap sensors that are used to capture a person’s acceleration and motion data in real scenarios without the use of external sources or devices [ 58 , 59 , 60 ], allowing to assess the activity performance through estimations of the kinematic and spatiotemporal parameters [ 61 ], and motion analysis [ 62 ]. Although it is possible to identify the person’s fatigue level using these sensors, considering that the kinematic study in fatigue is still an early topic, the use of other physiological parameters like blood lactate [ 63 ], EMG [ 64 ], or even perceived level of fatigue [ 65 ] are widely used to corroborate the results. A motion analysis system is widely used in fatigue estimation due to its high accuracy and robustness in the measurement of the kinematic parameters.…”

Section: Related Workmentioning

confidence: 99%

“…These sensors offer an individualized insight into the individuals’ performance and present a unified performance benchmark that does not depend on process cycle time (it is essential for real-time scenarios). Furthermore, we have chosen to focus on monitoring fatigue in walking tasks since (i) localized muscle fatigue is a potential risk factor for injury or falls as muscle fatigue adversely affects proprioception, movement coordination and muscle reaction times, leading to postural instability and gait alterations [ 65 , 69 , 73 , 76 , 77 ]; therefore, gait patterns associated with fatigue may help in the assessment of fatigue-related fall risks or injuries in various environments; and (ii) moderate-intensity training as walking exercises are one of the most used in physical rehabilitation process due to improving cardiovascular system and skeletal muscle function [ 20 , 22 , 23 , 78 ].…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

Pinto-Bernal

Cifuentes

Perdomo

et al. 2021

Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.