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

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6401
Author(s):  
Maria J. Pinto-Bernal ◽  
Carlos A. Cifuentes ◽  
Oscar Perdomo ◽  
Monica Rincón-Roncancio ◽  
Marcela Múnera
Keyword(s):  
Machine Learning ◽  
Physical Exercise ◽  
Learning Algorithm ◽  
Wearable Sensors ◽  
Algorithm Selection ◽  
Rehabilitation Programs ◽  
Fatigue Management ◽  
Average Accuracy ◽  
Data Driven Approach ◽  
Intensity Of Exercise

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%.

scholarly journals Human Activity Recognition of Children with Wearable Devices Using LightGBM Machine Learning

2021 ◽  
Author(s):  
Gábor Csizmadia ◽  
Krisztina Liszkai-Peres ◽  
Bence Ferdinandy ◽  
Ádám Miklósi ◽  
Veronika Konok
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Window Size ◽  
Human Activity Recognition ◽  
Daily Routine ◽  
Light Gradient

Abstract Human activity recognition (HAR) using machine learning (ML) methods is a relatively new method for collecting and analyzing large amounts of human behavioral data using special wearable sensors. Our main goal was to find a reliable method which could automatically detect various playful and daily routine activities in children. We defined 40 activities for ML recognition, and we collected activity motion data by means of wearable smartwatches with a special SensKid software. We analyzed the data of 34 children (19 girls, 15 boys; age range: 6.59 – 8.38; median age = 7.47). All children were typically developing first graders from three elementary schools. The activity recognition was a binary classification task which was evaluated with a Light Gradient Boosted Machine (LGBM)learning algorithm, a decision based method with a 3-fold cross validation. We used the sliding window technique during the signal processing, and we aimed at finding the best window size for the analysis of each behavior element to achieve the most effective settings. Seventeen activities out of 40 were successfully recognized with AUC values above 0.8. The window size had no significant effect. The overall accuracy was 0.95, which is at the top segment of the previously published similar HAR data. In summary, the LGBM is a very promising solution for HAR. In line with previous findings, our results provide a firm basis for a more precise and effective recognition system that can make human behavioral analysis faster and more objective.

scholarly journals Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1557 ◽  
Author(s):  
Ilaria Conforti ◽  
Ilaria Mileti ◽  
Zaccaria Del Prete ◽  
Eduardo Palermo
Keyword(s):  
Machine Learning ◽  
Material Handling ◽  
Learning Algorithm ◽  
Occupational Injuries ◽  
Wearable Sensors ◽  
High Stress ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Kinematic Parameters

Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

scholarly journals Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6413
Author(s):  
Victor A. Convertino ◽  
Steven G. Schauer ◽  
Erik K. Weitzel ◽  
Sylvain Cardin ◽  
Mark E. Stackle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Wearable Sensors ◽  
Clinical Status ◽  
Vital Signs ◽  
Human Model ◽  
Machine Learning Algorithm ◽  
Medical Monitoring ◽  
Arterial Waveform ◽  
Compensatory Reserve

Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings.

scholarly journals Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5573
Author(s):  
Mohsen Gholami ◽  
Christopher Napier ◽  
Astrid García Patiño ◽  
Tyler J. Cuthbert ◽  
Carlo Menon
Keyword(s):  
Machine Learning ◽  
Perceived Exertion ◽  
Learning Algorithm ◽  
Wearable Sensors ◽  
Human Movement ◽  
Strain Sensors ◽  
Coefficient Of Determination ◽  
Kinematic Data ◽  
Machine Learning Model ◽  
Gait Parameters

Fatigue is a multifunctional and complex phenomenon that affects how individuals perform an activity. Fatigue during running causes changes in normal gait parameters and increases the risk of injury. To address this problem, wearable sensors have been proposed as an unobtrusive and portable system to measure changes in human movement as a result of fatigue. Recently, a category of wearable devices that has gained attention is flexible textile strain sensors because of their ability to be woven into garments to measure kinematics. This study uses flexible textile strain sensors to continuously monitor the kinematics during running and uses a machine learning approach to estimate the level of fatigue during running. Five female participants used the sensor-instrumented garment while running to a state of fatigue. In addition to the kinematic data from the flexible textile strain sensors, the perceived level of exertion was monitored for each participant as an indication of their actual fatigue level. A stacked random forest machine learning model was used to estimate the perceived exertion levels from the kinematic data. The machine learning algorithm obtained a root mean squared value of 0.06 and a coefficient of determination of 0.96 in participant-specific scenarios. This study highlights the potential of flexible textile strain sensors to objectively estimate the level of fatigue during running by detecting slight perturbations in lower extremity kinematics. Future iterations of this technology may lead to real-time biofeedback applications that could reduce the risk of running-related overuse injuries.

scholarly journals Machine Learning Algorithms to Classify and Quantify Multiple Behaviours in Dairy Calves Using a Sensor: Moving beyond Classification in Precision Livestock

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 88
Author(s):  
Charles Carslake ◽  
Jorge A. Vázquez-Diosdado ◽  
Jasmeet Kaler
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Wearable Sensors ◽  
Window Size ◽  
Machine Learning Algorithms ◽  
Dairy Calves ◽  
Learning Approaches ◽  
True Prevalence ◽  
Play Behaviour ◽  
Highly Correlated

Previous research has shown that sensors monitoring lying behaviours and feeding can detect early signs of ill health in calves. There is evidence to suggest that monitoring change in a single behaviour might not be enough for disease prediction. In calves, multiple behaviours such as locomotor play, self-grooming, feeding and activity whilst lying are likely to be informative. However, these behaviours can occur rarely in the real world, which means simply counting behaviours based on the prediction of a classifier can lead to overestimation. Here, we equipped thirteen pre-weaned dairy calves with collar-mounted sensors and monitored their behaviour with video cameras. Behavioural observations were recorded and merged with sensor signals. Features were calculated for 1–10-s windows and an AdaBoost ensemble learning algorithm implemented to classify behaviours. Finally, we developed an adjusted count quantification algorithm to predict the prevalence of locomotor play behaviour on a test dataset with low true prevalence (0.27%). Our algorithm identified locomotor play (99.73% accuracy), self-grooming (98.18% accuracy), ruminating (94.47% accuracy), non-nutritive suckling (94.96% accuracy), nutritive suckling (96.44% accuracy), active lying (90.38% accuracy) and non-active lying (90.38% accuracy). Our results detail recommended sampling frequencies, feature selection and window size. The quantification estimates of locomotor play behaviour were highly correlated with the true prevalence (0.97; p < 0.001) with a total overestimation of 18.97%. This study is the first to implement machine learning approaches for multi-class behaviour identification as well as behaviour quantification in calves. This has potential to contribute towards new insights to evaluate the health and welfare in calves by use of wearable sensors.

scholarly journals Using Collective Knowledge to Assign Oxidation States

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Daniele Ongari ◽  
Seyed Mohamad Moosavi ◽  
Berend Smit
Keyword(s):  
Machine Learning ◽  
Oxidation State ◽  
Learning Algorithm ◽  
Oxidation States ◽  
Data Driven ◽  
Chemical Formula ◽  
Collective Knowledge ◽  
Metal Centre ◽  
Data Driven Approach ◽  
Metal Organic

<div><div><div><p>Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (> 98 %) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.</p></div></div></div>

scholarly journals Using Collective Knowledge to Assign Oxidation States

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Daniele Ongari ◽  
Seyed Mohamad Moosavi ◽  
Berend Smit
Keyword(s):  
Machine Learning ◽  
Oxidation State ◽  
Learning Algorithm ◽  
Oxidation States ◽  
Data Driven ◽  
Chemical Formula ◽  
Collective Knowledge ◽  
Metal Centre ◽  
Data Driven Approach ◽  
Metal Organic

<div><div><div><p>Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (> 98 %) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.</p></div></div></div>

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