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.

Four hundred million adults are obese. Such a pandemic involves people of working age. Excess weight imposes abnormal mechanics on body movements, which could account for the high incidence of musculoskeletal disorders in these subjects. This article reviews the physiological and biomechanical causes of the reduced work capacity in obese workers and speculates on the relationships between occupational exposure and obesity. The reduction in work capacity appears to be due to the following factors: reduced spine flexibility, decay in endurance, limited range of movement of the major joints, reduced muscle strength and capacity to hold prolonged fixed postures, impaired respiratory capacity and visual control. Work capacity in morbidly obese workers should always be evaluated to match specific job demands. Due to the relationship between obesity, musculoskeletal disorders, disability and health costs, prevention of obesity and ergonomic interventions on-site are a priority in the work place.

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.

This paper presents a new wearable e-textile based system, named SWEET Sock, for biomedical signals remote monitoring. The system includes a textile sensing sock, an electronic unit for data transmission, a custom-made Android application for real-time signal visualization, and a software desktop for advanced digital signal processing. The device allows the acquisition of angular velocities of the lower limbs and plantar pressure signals, which are postprocessed to have a complete and schematic overview of patient’s clinical status, regarding gait and postural assessment. In this work, device performances are validated by evaluating the agreement between the prototype and an optoelectronic system for gait analysis on a set of free walk acquisitions. Results show good agreement between the systems in the assessment of gait cycle time and cadence, while the presence of systematic and proportional errors are pointed out for swing and stance time parameters. Worse results were obtained in the comparison of spatial metrics. The “wearability” of the system and its comfortable use make it suitable to be used in domestic environment for the continuous remote health monitoring of de-hospitalized patients but also in the ergonomic assessment of health workers, thanks to its low invasiveness.

Background: supervised training can reach a limited number of elderly people. Objective: to determine the impact of a 1-year mixed-strength training programme on muscle function (MF), functional ability (FA) and physical activity (PA). Setting: twice-a-week hospital-based exercise classes and a once-a-week home session. Participants: twenty-eight healthy community-dwelling men and women on the training programme and 20 controls aged over 75 years. Methods: training with two multi-gym machines for the lower limbs at 60% of the repetition maximum (1RM). At-home subjects used elastic bands. Measurements: maximum isometric strength of knee extensors (KE), ankle plantar flexors (PF), leg extensor power (LEP), functional reach (FR), chair rise 1 (CR1) and 10 times (CR10), bed rise (BR), six-minute walking test (6MWT), stair climbing (SC), get-up-and-go (GU&G), one-leg standing (1LS). PA was assessed with the Paqap^© questionnaire. Results: women were significantly weaker than men at baseline: −47% for KE and −59% for PF. Training induced significant gains in MF and FA in the training females; males improved significantly only in FA. PA levels increased non-significantly (2%) in all of the training group. Conclusions: long-term mixed-strength programmes can improve MF and FA in elderly females, and FA in elderly males.