Functional Limitations and Occupational Issues in Obesity: A Review

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Job preservation by an office worker with idiopathic cervical dystonia: case report

Background: Work preservation is a main goal in the rehabilitation of chronic disabling diseases. We describe the application of an interdisciplinary protocol, involving the occupational therapist and the ergonomist, in the case of a 50 year-old office worker with idiopathic cervical dystonia, a movement disorder that can seriously impair work capability. Case report: The disease was diagnosed at age 25, and subsequently worsened. The man presented postural difficulties and pain. The symptomatology worsened during working shifts, preventing him from doing his job properly. Functional evaluation and ergonomic inspection of the office environment led to the correction of evident critical inadequacies. This allowed the patient to continue working in correct conditions, resulting in improvement of his global health status. Conclusions: The interdisciplinary rehabilitative approach here described may allow subjects with idiopathic cervical dystonia to keep their jobs by adapting the workplace to the changed physical capabilities

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

: Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity

A multiple linear regression approach to extimate lifted load from features extracted from inertial data

: Work-related musculoskeletal disorders are among the main occupational health problems. Substantial evidence has shown that work-related physical risk factors are the main source of low back complaints, particularly affecting heavy and repetitive manual lifting activities. The aim of the study is, during load lifting tasks, to explore the correlation between the time domain features extracted from the acceleration and angular velocity signals of the performing subject and the load lifted, and to explore the feasibility of a multiple linear regression model to predict the lifted load. The acceleration and angular velocity signals were acquired along the three directions of space by means of an inertial sensor placed on the subject's chest, during lifting activities with load gradually increased by 1 kg from 0 kg to 18 kg. Successively three time-domain features (Root Mean Square, Standard Deviation and MinMax value) were extracted from the acquired signals. First a correlation analysis was carried out between each individual feature and the load lifted (calculating r); then the time-domain features that proved most representative (strong correlation) were used to create a multiple linear regression model (calculating R-square). The statistical analysis was carried out by means of the Pearson correlation and multiple linear regression model was fed with the most informative time-domain features according to the correlation analysis. The correlation analysis showed a strong correlation (r > 0,7) between six features (three extracted from z-axes acceleration and three extracted from y-axes angular velocity) and the lifted load. The predictive multiple linear regression model, fed with these six features achieved a Rsquare greater than 0,9.The study demonstrated that the proposed combination of kinematic features and a multiple regression model represents a valid approach to automatically calculate the load lifted based on raw signals obtained by means of an inertial sensor placed on the chest. The results confirm the potential application of this methodology to indirectly monitor the load lifted by workers during their activity

A multiple linear regression approach to extimate lifted load from features extracted from inertial data

: Work-related musculoskeletal disorders are among the main occupational health problems. Substantial evidence has shown that work-related physical risk factors are the main source of low back complaints, particularly affecting heavy and repetitive manual lifting activities. The aim of the study is, during load lifting tasks, to explore the correlation between the time domain features extracted from the acceleration and angular velocity signals of the performing subject and the load lifted, and to explore the feasibility of a multiple linear regression model to predict the lifted load. The acceleration and angular velocity signals were acquired along the three directions of space by means of an inertial sensor placed on the subject's chest, during lifting activities with load gradually increased by 1 kg from 0 kg to 18 kg. Successively three time-domain features (Root Mean Square, Standard Deviation and MinMax value) were extracted from the acquired signals. First a correlation analysis was carried out between each individual feature and the load lifted (calculating r); then the time-domain features that proved most representative (strong correlation) were used to create a multiple linear regression model (calculating R-square). The statistical analysis was carried out by means of the Pearson correlation and multiple linear regression model was fed with the most informative time-domain features according to the correlation analysis. The correlation analysis showed a strong correlation (r > 0,7) between six features (three extracted from z-axes acceleration and three extracted from y-axes angular velocity) and the lifted load. The predictive multiple linear regression model, fed with these six features achieved a Rsquare greater than 0,9.The study demonstrated that the proposed combination of kinematic features and a multiple regression model represents a valid approach to automatically calculate the load lifted based on raw signals obtained by means of an inertial sensor placed on the chest. The results confirm the potential application of this methodology to indirectly monitor the load lifted by workers during their activity

A multiple linear regression approach to extimate lifted load from features extracted from inertial data

: Work-related musculoskeletal disorders are among the main occupational health problems. Substantial evidence has shown that work-related physical risk factors are the main source of low back complaints, particularly affecting heavy and repetitive manual lifting activities. The aim of the study is, during load lifting tasks, to explore the correlation between the time domain features extracted from the acceleration and angular velocity signals of the performing subject and the load lifted, and to explore the feasibility of a multiple linear regression model to predict the lifted load. The acceleration and angular velocity signals were acquired along the three directions of space by means of an inertial sensor placed on the subject's chest, during lifting activities with load gradually increased by 1 kg from 0 kg to 18 kg. Successively three time-domain features (Root Mean Square, Standard Deviation and MinMax value) were extracted from the acquired signals. First a correlation analysis was carried out between each individual feature and the load lifted (calculating r); then the time-domain features that proved most representative (strong correlation) were used to create a multiple linear regression model (calculating R-square). The statistical analysis was carried out by means of the Pearson correlation and multiple linear regression model was fed with the most informative time-domain features according to the correlation analysis. The correlation analysis showed a strong correlation (r > 0,7) between six features (three extracted from z-axes acceleration and three extracted from y-axes angular velocity) and the lifted load. The predictive multiple linear regression model, fed with these six features achieved a Rsquare greater than 0,9.The study demonstrated that the proposed combination of kinematic features and a multiple regression model represents a valid approach to automatically calculate the load lifted based on raw signals obtained by means of an inertial sensor placed on the chest. The results confirm the potential application of this methodology to indirectly monitor the load lifted by workers during their activity

Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature

Physical ergonomics has established itself as a valid strategy for monitoring potential disorders related, for example, to working activities. Recently, in the field of physical ergonomics, several studies have also shown potential for improvement in experimental methods of ergonomic analysis, through the combined use of artificial intelligence, and wearable sensors. In this regard, this review intends to provide a first account of the investigations carried out using these combined methods, considering the period up to 2021. The method that combines the information obtained on the worker through physical sensors (IMU, accelerometer, gyroscope, etc.) or biopotential sensors (EMG, EEG, EKG/ECG), with the analysis through artificial intelligence systems (machine learning or deep learning), offers interesting perspectives from both diagnostic, prognostic, and preventive points of view. In particular, the signals, obtained from wearable sensors for the recognition and categorization of the postural and biomechanical load of the worker, can be processed to formulate interesting algorithms for applications in the preventive field (especially with respect to musculoskeletal disorders), and with high statistical power. For Ergonomics, but also for Occupational Medicine, these applications improve the knowledge of the limits of the human organism, helping in the definition of sustainability thresholds, and in the ergonomic design of environments, tools, and work organization. The growth prospects for this research area are the refinement of the procedures for the detection and processing of signals; the expansion of the study to assisted working methods (assistive robots, exoskeletons), and to categories of workers suffering from pathologies or disabilities; as well as the development of risk assessment systems that exceed those currently used in ergonomics in precision and agility