Occupational physical activity assessment for chronic disease prevention and management: A review of methods for both occupational health practitioners and researchers

A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor

Existing ergonomic risk assessment tools require monitoring of multiple risk factors. To eliminate the direct observation, we investigated the effectiveness of an end-to-end framework that works with the data from a single wearable sensor. The framework is used to identify the performed task as the major contextual risk factor, and then estimate the task duration and number of repetitions as two main indicators of task intensity. For evaluation of the framework, we recruited 37 participants to complete 10 simulated work tasks in a laboratory setting. In testing, we achieved an average accuracy of 92% for task identification, 7.3% error in estimation of task duration, and 7.1% error for counting the number of task repetitions. Moreover, we showed the utility of the framework outputs in two ergonomic tools to estimate the risk of injury. Overall, we indicated the feasibility of using data from wearable sensors to automate the ergonomic risk assessment in workplaces.

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.

Measurement of Physical Activity by Shoe-Based Accelerometers-Calibration and Free-Living Validation

There is conflicting evidence regarding the health implications of high occupational physical activity (PA). Shoe-based accelerometers could provide a feasible solution for PA measurement in workplace settings. This study aimed to develop calibration models for estimation of energy expenditure (EE) from shoe-based accelerometers, validate the performance in a workplace setting and compare it to the most commonly used accelerometer positions. Models for EE estimation were calibrated in a laboratory setting for the shoe, hip, thigh and wrist worn accelerometers. These models were validated in a free-living workplace setting. Furthermore, additional models were developed from free-living data. All sensor positions performed well in the laboratory setting. When the calibration models derived from laboratory data were validated in free living, the shoe, hip and thigh sensors displayed higher correlation, but lower agreement, with measured EE compared to the wrist sensor. Using free-living data for calibration improved the agreement of the shoe, hip and thigh sensors. This study suggests that the performance of a shoe-based accelerometer is similar to the most commonly used sensor positions with regard to PA measurement. Furthermore, it highlights limitations in using the relationship between accelerometer output and EE from a laboratory setting to estimate EE in a free-living setting.

Workplace activity classification from shoe-based movement sensors

Background

High occupational physical activity is associated with lower health. Shoe-based movement sensors can provide an objective measurement of occupational physical activity in a lab setting but the performance of such methods in a free-living environment have not been investigated. The aim of the current study was to investigate the feasibility and accuracy of shoe sensor-based activity classification in an industrial work setting.

Results

An initial calibration part was performed with 35 subjects who performed different workplace activities in a structured lab setting while the movement was measured by a shoe-sensor. Three different machine-learning models (random forest (RF), support vector machine and k-nearest neighbour) were trained to classify activities using the collected lab data. In a second validation part, 29 industry workers were followed at work while an observer noted their activities and the movement was captured with a shoe-based movement sensor. The performance of the trained classification models were validated using the free-living workplace data. The RF classifier consistently outperformed the other models with a substantial difference in in the free-living validation. The accuracy of the initial RF classifier was 83% in the lab setting and 43% in the free-living validation. After combining activities that was difficult to discriminate the accuracy increased to 96 and 71% in the lab and free-living setting respectively. In the free-living part, 99% of the collected samples either consisted of stationary activities or walking.

Conclusions

Walking and stationary activities can be classified with high accuracy from a shoe-based movement sensor in a free-living occupational setting. The distribution of activities at the workplace should be considered when validating activity classification models in a free-living setting.

Occupational Risk Prevention through Smartwatches: Precision and Uncertainty Effects of the Built-In Accelerometer

Wearable technology has had a significant growth in the last years; this is particularly true of smartwatches, due to their potential advantages and ease of use. These smart devices integrate sensors that can be potentially used within industrial settings and for several applications, such as safety, monitoring, and the identification of occupational risks. The accelerometer is one of the main sensors integrated into these devices. However, several studies have identified that sensors integrated into smart devices may present inaccuracies during data acquisition, which may influence the performance of their potential applications. This article presents an analysis from the metrological point of view to characterize the amplitude and frequency response of the integrated accelerometers in three currently available commercial smartwatches, and it also includes an analysis of the uncertainties associated with these measurements by adapting the procedures described in several International Organization for Standardization (ISO) standards. The results show that despite the technical limitations produced by the factory configuration, these devices can be used in various applications related to occupational risk assessment. Opportunities for improvement have also been identified, which will allow us to take advantage of this technology in several innovative applications within industrial settings and, in particular, for occupational health purposes.

Occupational physical activity in brewery and office workers

Active lifestyles are beneficial to health and well-being but our workplaces may not be inherently supportive of physical activity at work. With the increasing use of technology in the workplace, many jobs are becoming more sedentary. The purpose of this study was to characterize levels of occupational physical activity (OPA) among active and sedentary workers. Two types of activity trackers (Fitbit Charge HR and Hexoskin) were used to assess activity measures (steps, heart rate, and energy expenditure) among workers during one full work shift. The first objective of the study was to assess the agreement between two types of accelerometer-based activity trackers as measures of occupational physical activity. The second objective of this study was to assess differences in measures of OPA among workers in generally physically active (brewery) and sedentary (office) work environments. Occupational physical activity data were collected from 50 workers in beer-brewing tasks and 51 workers in office work tasks. The 101 subjects were from the brewing service sector, a call center, and an engineering office within a manufacturing facility. A two-factor repeated measures analysis of variance (ANOVA) was used to assess the two activity tracking devices while two-sample t-tests were used to compare the two worker groups. There were statistically significant differences in total steps and mean heart rate between the two devices. When comparing the two groups of workers there were statistically significant differences in measures of steps, mean heart rate, and energy expenditure. The results of the present study provide quantitative evidence that levels of OPA should be identified for different work groups.

Relations between subdomains of physical activity, sedentary lifestyle, and quality of life in young adult men

To assess the relationship between physical activity (PA) in work, transport, domestic, and leisure-time domains (with sitting time included) and health-related quality of life (HRQoL) among young adult men. The long version of IPAQ and SF-36 Health Survey were used to assess PA and HRQoL, respectively, in 1425 voluntary 20- to 40-year-old Finnish male participants. Participants were divided into tertiles (MET-h/week): Lowest tertile (<38 MET-h/week), Middle tertile (38-100 MET-h/week), and Highest tertile (>100 MET-h/week). The IPAQ domain leisure-time PA predicted positively the Physical Component Summary (PCS) (β = 0.11, 95% CI: 0.06 to 0.16) and Mental Component Summary (MCS) (β = 0.11, 95% CI: 0.05 to 0.16) dimensions. Occupational PA predicted negative relationships in the PCS (β = -0.13, 95% CI: -0.19 to -0.07), and sitting time predicted negative relationships in the MCS dimension (β = -0.13, 95% CI: -0.18 to -0.07). In addition, a linear relationship was found between total PA level (including sitting time) and all of the IPAQ domains (<0.001). The Middle tertile had the highest leisure-time PA (38% of total PA), whereas the highest sitting time (28%) and lowest occupational PA (8%) were found in the Lowest tertile. The Highest tertile had the highest occupational PA (61%), while the leisure-time PA was the lowest (16%). Different PA domains appear to have positive and negative relationships to mental and physical aspects of HRQoL. Relatively high leisure-time PA indicated a better HRQoL regardless of the amount of total PA, while occupational PA and higher daily sitting time related negatively to HRQoL.