Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors.

Occupational ergonomics aims to optimize the work environment and to enhance both productivity and worker well-being. Work-related exposure assessment, such as lifting loads, is a crucial aspect of this discipline, as it involves the evaluation of physical stressors and their impact on workers' health and safety, in order to prevent the development of musculoskeletal pathologies. In this study, we explore the feasibility of machine learning (ML) algorithms, fed with time- and frequency-domain features extracted from inertial signals (linear acceleration and angular velocity), to automatically and accurately discriminate safe and unsafe postures during weight lifting tasks. The signals were acquired by means of one inertial measurement unit (IMU) placed on the sternums of 15 subjects, and subsequently segmented to extract several time- and frequency-domain features. A supervised dataset, including the extracted features, was used to feed several ML models and to assess their prediction power. Interesting results in terms of evaluation metrics for a binary safe/unsafe posture classification were obtained with the logistic regression algorithm, which outperformed the others, with accuracy and area under the receiver operating characteristic curve values of up to 96% and 99%, respectively. This result indicates the feasibility of the proposed methodology-based on a single inertial sensor and artificial intelligence-to discriminate safe/unsafe postures associated with load lifting activities. Future investigation in a wider study population and using additional lifting scenarios could confirm the potentiality of the proposed methodology, supporting its applicability in the occupational ergonomics field.

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings.

Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology-based on wearable sensors and artificial intelligence-to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.

SAR and efficiency evaluation of a 900 MHz waveguide chamber for cell exposure.

In this work we present the results of numerical and experimental dosimetry carried out for an in vitro exposure device to irradiate sample groups at 900 MHz. The cells are kept in 8 and 15 ml cell cultures, contained, respectively in T25 and T75 rectangular flasks. The dosimetric assessment of the distribution of the specific absorption rate (SAR) is performed for both the bottom of the flask and the whole volume of the sample to provide results for experiments on either the cell layer or the cell suspension. The irradiating chamber is a rectangular waveguide (WG). Different configurations are considered to assess the optimum orientation and positioning of the cell cultures inside the WG. The system performance is optimal when the electric field is parallel to the sample and the WG is terminated by a matched load. In this condition two 15 or four 8 ml cells cultures can be exposed. The efficiency (ratio between the power absorbed by the sample and the incident power) and the non-uniformity degree (ratio between the standard deviation of SAR values and the average SAR over the sample) are calculated and successfully verified through measurements of the scattering parameters and local temperature increases. In the chosen exposure configuration, the efficiency is 0.40 and the non-uniformity degree is 39% for the 15 ml samples. For the 8 ml samples, the efficiency is 0.19 and a low non-uniformity degree (15%) is found.