Evaluation of four sensor locations for physical activity assessment

Manufacturing worker perceptions of using wearable inertial sensors for multiple work shifts

Wearable inertial sensors may be used to objectively quantify exposure to some physical risk factors associated with musculoskeletal disorders. However, concerns regarding their potential negative effects on user safety and satisfaction remain. This study characterized the self-reported daily discomfort, distraction, and burden associated with wearing inertial sensors on the upper arms, trunk, and dominant wrist of 31 manufacturing workers collected over 15 full work shifts. Results indicated that the workers considered the devices as generally comfortable to wear, not distracting, and not burdensome to use. Exposure to non-neutral postures (discomfort, right arm, beta = 0.02; trunk, beta = -0.01), non-cyclic tasks (distraction, beta = -0.26), and higher body mass indices (discomfort, beta = 0.05; distraction, beta = 0.02) contributed to statistically significant (p < 0.05), albeit practically small increases in undesirable ratings. For instance, for each additional percentage of time working with the right arm elevated ≥60°, self-reported discomfort ratings increased 0.02 cm on a standard 10 cm visual analog scale. Female workers reported less discomfort and distraction while wearing the sensors at work than males (discomfort, beta = -0.93; distraction, beta = -0.3). In general, the low ratings of discomfort, distraction, and burden associated with wearing the devices during work suggests that inertial sensors may be suitable for extended use among manufacturing workers.

Plantar fasciitis in physicians and nurses: a nationwide population-based study

Physicians and nurses in Taiwan have heavy workload and long working hours, which may contribute to plantar fasciitis. However, this issue is unclear, and therefore, we conducted this study to delineate it. We conducted a nationwide population-based study by identifying 26,024 physicians and 127,455 nurses and an identical number of subjects for comparison (general population) via the National Health Insurance Research Database. The risk of plantar fasciitis between 2006 and 2012 was compared between physicians and general population, between nurses and general population, and between physicians and nurses. We also compared the risk of plantar fasciitis among physician subgroups. Physicians and nurses had a period prevalence of plantar fasciitis of 8.14% and 13.11% during the 7-yr period, respectively. The risk of plantar fasciitis was lower among physicians (odds ratio [OR]: 0.660; 95% confidence interval [CI]: 0.622-0.699) but higher among nurses (OR: 1.035; 95% CI: 1.011-1.059) compared with that in the general population. Nurses also had a higher risk than the physicians after adjusting for age and sex (adjusted odds ratio [AOR]: 1.541; 95% CI: 1.399-1.701). Physician subspecialties of orthopedics and physical medicine and rehabilitation showed a higher risk. Female physicians had a higher risk of plantar fasciitis than male physicians. This study showed that nurses, physician specialties of orthopedics and physical medicine and rehabilitation, and female physicians had a higher risk of plantar fasciitis. Improvement of the occupational environment and health promotion are suggested for these populations.

How many calories do nurses burn at work? A real-time study of nurses' energy expenditure

Background

Nurses have high rates of overweight and obesity, and physical inactivity is one key determinant of weight gain.

Aims

The present study aims to quantify nurses' energy expenditure over a standard 12-hour shift to establish whether activity levels at work are too low to maintain a healthy body weight.

Methods

Ward-based nurses (n = 96, 90 female, mean age = 36 years, mean experience = 10 years, mean body mass index = 26.6) wore heart rate and physical activity monitors for the duration of one full working shift. Heart rate and activity data were used in combination with demographic information to estimate each nurse's energy expenditure over 12 hours.

Results

On average, nurses burned 2.12 kcals per minute while at work, equating to 1521 kcals/6364 kJ (SD = 403 kcals/1686 kJ) per 12-hour shift. Energy expenditure over 12 hours varied markedly between nurses (from 812 kcals/3397 kJ to 3005 kcals/12,573 kJ) but was sufficient in 72% of participants (assuming minimal resting levels of energy expenditure for the 12 hours not at work) to burn off a daily calorie intake of 2000 kcals.

Conclusions

Three-quarters of nurses expend more energy on working days than is required to maintain a healthy body weight if dietary intake does not exceed recommended levels.

Calibration and validation of accelerometer-based activity monitors: A systematic review of machine-learning approaches

BACKGROUND

Objective measures using accelerometer-based activity monitors have been extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting towards machine learning-based (ML) approaches for calibration and validation of accelerometer-based activity monitors. Nevertheless, various parameters regarding the use and development of ML-based models, including data type (raw acceleration data versus activity counts), sampling frequency, window size, input features, ML technique, accelerometer placement, and free-living settings, affect the predictive ability of ML-based models. The effects of these parameters on ML-based models have remained elusive, and will be systematically reviewed here. The open challenges were identified and recommendations are made for future studies and directions.

METHOD

We conducted a systematic search of PubMed and Scopus databases to identify studies published before July 2017 that used ML-based techniques for calibration and validation of accelerometer-based activity monitors. Additional articles were manually identified from references in the identified articles.

RESULTS

A total of 62 studies were eligible to be included in the review, comprising 48 studies that calibrated and validated ML-based models for predicting the type and intensity of activities, and 22 studies for predicting activity energy expenditure.

CONCLUSIONS

It appears that various ML-based techniques together with raw acceleration data sampled at 20-30 Hz provide the opportunity of predicting the type and intensity of activities, as well as activity energy expenditure with comparable overall predictive accuracies regardless of accelerometer placement. However, the high predictive accuracy of laboratory-calibrated models is not reproducible in free-living settings, due to transitive and unseen activities together with differences in acceleration signals.

Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring

Wearable sensors and systems have become increasingly popular in recent years. Two prominent wearable technologies for human activity monitoring are smart textile systems (STSs) and inertial measurement units (IMUs). Despite ongoing advances in both, the usability aspects of these devices require further investigation, especially to facilitate future use. In this study, 18 participants evaluate the preferred placement and usability of two STSs, along with a comparison to a commercial IMU system. These evaluations are completed after participants engaged in a range of activities (e.g., sitting, standing, walking, and running), during which they wear two representatives of smart textile systems: (1) a custom smart undershirt (SUS) and commercial smart socks; and (2) a commercial whole-body IMU system. We first analyze responses regarding the usability of the STS, and subsequently compared these results to those for the IMU system. Participants identify a short-sleeved shirt as their preferred activity monitor. In additional, the SUS in combination with the smart socks is rated superior to the IMU system in several aspects of usability. As reported herein, STSs show promise for future applications in human activity monitoring in terms of usability.

An evaluation of wearable sensors and their placements for analyzing construction worker's trunk posture in laboratory conditions

This study investigates the effect of sensor placement on the analysis of trunk posture for construction activities using two off-the-shelf systems. Experiments were performed using a single-parameter monitoring wearable sensor (SPMWS), the ActiGraph GT9X Link, which was worn at six locations on the body, and a multi-parameter monitoring wearable sensor (MPMWS), the Zephyr BioHarness™3, which was worn at two body positions. One healthy male was recruited and conducted 10 experiment sessions to repeat measurements of trunk posture within our study. Measurements of upper-body thoracic bending posture during the lifting and lowering of raised deck materials in a laboratory setting were compared against video-captured observations of posture. The measurements from the two sensors were found to be in agreement during slow-motion symmetric bending activities with a target bending of ≤45°. However, for asymmetric bending tasks, when the SPMWS was placed on the chest, its readings were substantially different from those of the MPMWS worn on the chest or under the armpit.

A data-driven approach to modeling physical fatigue in the workplace using wearable sensors

Wearable sensors are currently being used to manage fatigue in professional athletics, transportation and mining industries. In manufacturing, physical fatigue is a challenging ergonomic/safety "issue" since it lowers productivity and increases the incidence of accidents. Therefore, physical fatigue must be managed. There are two main goals for this study. First, we examine the use of wearable sensors to detect physical fatigue occurrence in simulated manufacturing tasks. The second goal is to estimate the physical fatigue level over time. In order to achieve these goals, sensory data were recorded for eight healthy participants. Penalized logistic and multiple linear regression models were used for physical fatigue detection and level estimation, respectively. Important features from the five sensors locations were selected using Least Absolute Shrinkage and Selection Operator (LASSO), a popular variable selection methodology. The results show that the LASSO model performed well for both physical fatigue detection and modeling. The modeling approach is not participant and/or workload regime specific and thus can be adopted for other applications.

Monitoring sedentary patterns in office employees: validity of an m-health tool (Walk@Work-App) for occupational health

OBJECTIVE

This study validated the Walk@Work-Application (W@W-App) for measuring occupational sitting and stepping.

METHODS

The W@W-App was installed on the smartphones of office-based employees (n=17; 10 women; 26±3 years). A prescribed 1-hour laboratory protocol plus two continuous hours of occupational free-living activities were performed. Intra-class correlation coefficients (ICC) compared mean differences of sitting time and step count measurements between the W@W-App and criterion measures (ActivPAL3TM and SW200Yamax Digi-Walker).

RESULTS

During the protocol, agreement between self-paced walking (ICC=0.85) and active working tasks step counts (ICC=0.80) was good. The smallest median difference was for sitting time (1.5seconds). During free-living conditions, sitting time (ICC=0.99) and stepping (ICC=0.92) showed excellent agreement, with a difference of 0.5minutes and 18 steps respectively.

CONCLUSIONS

The W@W-App provided valid measures for monitoring occupational sedentary patterns in real life conditions; a key issue for increasing awareness and changing occupational sedentariness.

Musculoskeletal disorders as a fatigue failure process: evidence, implications and research needs

Mounting evidence suggests that musculoskeletal disorders (MSDs) may be the result of a fatigue failure process in musculoskeletal tissues. Evaluations of MSD risk in epidemiological studies and current MSD risk assessment tools, however, have not yet incorporated important principles of fatigue failure analysis in their appraisals of MSD risk. This article examines the evidence suggesting that fatigue failure may play an important role in the aetiology of MSDs, assesses important implications with respect to MSD risk assessment and discusses research needs that may be required to advance the scientific community's ability to more effectively prevent the development of MSDs. Practitioner Summary: Evidence suggests that musculoskeletal disorders (MSDs) may result from a fatigue failure process. This article proposes a unifying framework that aims to explain why exposure to physical risk factors contributes to the development of work-related MSDs. Implications of that framework are discussed.

Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions.

Musculoskeletal Problems Among Greek Perioperative Nurses in Regional Hospitals in Southern Peloponnese : Musculoskeletal Problems in Perioperative Nurses

The surgery unit is a particularly labor-intensive environment in the hospital. Studies reflect the correlation of labor risk factors for musculoskeletal injuries among nurses but few have investigated the relationship to perioperative nurses. The purpose of this study is the identification and definition of ergonomic risk factors in the operating room and their connection with musculoskeletal disorders in perioperative nurses in regional hospitals in Greece. Forty four Greek perioperative nurses working in regional hospitals in southern Peloponnese participated. Anonymous self-administered questionnaire was used to collect the data, which consisted of three parts (investigating musculoskeletal symptoms, description of work, psychometric evaluation). The analysis was done with the statistical program SPSS.19. Symptoms of musculoskeletal problems emerged. Specifically, 54.4% in the lumbar, 47.7% in the neck, 45.5% in the shoulder, followed by smaller percentages of the hip, knee, elbow and ankle. 6.8% of participants indicated no musculoskeletal symptoms in the last year while 74.9% of those who had symptoms presented them in two or more areas. Activities rated as a major problem among others were the manual handling, tools with weight and vibration etc. 100% of respondents agreed that the work in the surgery unit is demanding and has anxiety. The lack of support from the government (81.8%), combined with the low perioperative nurses (6.8%) having the opportunity to participate in administrative decisions concerning them were related to problems in the organization and management of work. Apart from engineers target factors, a main aim should be the organization of work within the framework of a national policy based on European directives on the protection and promotion of the health and safety of workers.

Working postures and physical activity among registered nurses

Nurses report a high prevalence of musculoskeletal discomfort, particularly of the low back and neck/shoulder. This study characterized the full-shift upper arm and trunk postures and movement velocities of registered nurses using inertial measurement units (IMUs). Intensity of occupational physical activity (PA) was also ascertained using a waist-worn PA monitor and using the raw acceleration data from each IMU. Results indicated that nurses spent a relatively small proportion of their work time with the arms or trunk in extreme postures, but had few opportunities for rest and recovery in comparison to several other occupational groups. Comparisons between nurses in different PA groups suggested that using a combination of accelerometers secured to several body locations may provide more representative estimates of physical demands than a single, waist-worn PA monitor. The findings indicate a need for continued field-based research with larger sample sizes to facilitate the development of maximally effective intervention strategies.