Fortune, Emma

A workplace that is a conduit for positive emotions can be important to employees retention and can contribute optimal levels of productivity. Validated tools for examining emotions are primarily subjective and retrospective in nature. Recent advances in technology have led to more novel and passive ways of measuring emotions. Wearable sensors, such as electroencephalogram (EEG), are being explored to assess cognitive and physical burdens objectively and in real-time. Therefore, there exists a need to investigate and validate the use of EEG to examine emotions objectively and in real-time. In this paper, we conducted a scoping review of EEG to measure positive emotions and/or indicators of joy in the workplace. Our review results in 22 articles that employ EEG to study joy in occupational settings. Three major themes identified in the analysis include (1) EEG for symptoms detection and outcomes, (2) Populations studied using EEG, and (3) EEG electrode systems.

Arm use in manual wheelchair (MWC) users is characterized by a combination of overuse and a sedentary lifestyle. This study aimed to describe the percentage of daily time MWC users and able-bodied individuals spend in each arm use intensity level utilizing accelerometers. Arm use intensity levels of the upper arms were defined as stationary, low, mid, and high from the signal magnitude area (SMA) of the segment accelerations based on in-lab MWC activities performed by eight MWC users. Accelerometry data were collected in the free-living environments from forty MWC users and 40 sex- and age-matched able-bodied individuals. The SMA intensity levels were applied to the free-living data and the percentage of time spent in each level was calculated. The SMA intensity levels were defined as, stationary: ≤0.67 g, low: 0.671–3.27 g, mid: 3.27–5.87 g, and high: >5.871 g. The dominant arm of both MWC users and able-bodied individuals was stationary for most of the day and less than one percent of the day was spent in high intensity arm activities. Increased MWC user age correlated with increased stationary arm time (R = 0.368, p = 0.019). Five and eight days of data are needed from MWC users and able-bodied individuals, respectively, to achieve reliable representation of their daily arm use intensities.

Abstract A working environment which elicits positive emotions in employees is vital for employee retention, engagement and productivity. Wearable sensors provide the means to objectively measure the emotional responses of employees in the workplace in real-time. The study aim was to perform a preliminary investigation into the validity of two multimodal systems to classify employee’s emotional responses to positive, neutral or negative video stimuli: (1) using wearable electroencephalography (EEG) in combination with video-based facial expression analysis (FEA), and (2) using a wearable galvanic skin response (GSR) device in combination with video-based FEA. Five office employees each watched three short video clips at three time points during their regular work shifts while wearing EEG sensors on the forehead and GSR sensors on the middle and index fingers of their non-dominant hand with their face in view of a webcam. Russel’s circumplex model of affect was used to determine participant’s emotional responses to the video clips. The GSR device showed greater accuracy than the EEG device at detecting arousal responses to the video stimuli, with agreement, precision, and recall values of 87%, 100% and 80%, respectively, compared to 53%, 62%, and 80% for the EEG device. The FEA/EEG and FEA/GSR circumplex models were both able to accurately detect positive emotions elicited from video stimuli with levels of agreement and recall greater than 73%. Precision for the FEA/EEG model to detect positive stimuli was lower due to misclassification of 40% of both negative and neutral stimuli as positive. Precision values for both circumplex models were very low for detecting negative emotions. The results suggest that the EEG and GSR devices may be capable of detecting arousal when used alone, and detecting positive emotions when used in combination with video-based FEA in real-time in the workplace.

Abstract Objective: Increasing physical activity (PA) is regularly cited as a modifiable target to improve health outcomes and quality of life in the aging population, especially postmenopausal women who exhibit low bone mineral density (BMD) and high fracture risk. In this cross-sectional study, we aimed to quantify real-world PA and its association with BMD in postmenopausal women. Methods: Seventy postmenopausal women, aged 46 to 79 years, received a dual-energy X-ray absorptiometry scan measuring total hip BMD and wore bilateral triaxial accelerometers on the ankles for 7 days to measure PA in their free-living environment. Custom step detection and peak vertical ground reaction force estimation algorithms, sensitive to both quantity and intensity of PA, were used to calculate a daily bone density index (BDI) for each participant. Multiple regression was used to quantify the relationship between total hip BMD, age, step counts, and mean BDI over the span of 7 days of data collection. Results: All participants completed the full 7 days of PA monitoring, totaling more than 7 million detected steps. Participants averaged 14,485 ± 4,334 steps daily with mean peak vertical ground reaction force stepping loads of 675 ± 121 N during daily living. Across the population, total hip BMD was found to be significantly correlated with objective estimates of mean BDI (r = 0.44), as well as participant age (r = 0.285). Conclusion: Despite having higher-than-expected PA, the low stepping loads observed in this cohort, along with half of the participants having low BMD measures, underscores the need for PA intensity to be considered in the management of postmenopausal bone health.

The purpose of this study was to validate a commercially available inertial measurement unit (IMU) system against a standard lab-based motion capture system for the measurement of shoulder elevation, elbow flexion, trunk flexion/extension, and neck flexion/extension kinematics. The validation analyses were applied to 6 surgical faculty members performing a standard, simulated surgical training task that mimics minimally invasive surgery. Three-dimensional joint kinematics were simultaneously recorded by an optical motion capture system and an IMU system with 6 sensors placed on the head, chest, and bilateral upper and lower arms. The sensor-to-segment axes alignment was accomplished manually. The IMU neck and trunk IMU flexion/extension angles were accurate to within 2.9 ± 0.9 degrees and 1.6 ± 1.1°, respectively. The IMU shoulder elevation measure was accurate to within 6.8 ± 2.7° and the elbow flexion measure was accurate to within 8.2 ± 2.8°. In the Bland-Altman analyses, there were no significant systematic errors present; however, there was a significant inversely proportional error across all joints. As the gold standard measurement increased, the IMU underestimated the magnitude of the joint angle. This study reports acceptable accuracy of a commercially available IMU system; however, results should be interpreted as protocol specific.

Repeated durations of dynamic activity with high ground reaction forces (GRFs) and loading rates (LRs) can be beneficial to bone health. To fully characterize dynamic activity in relation to bone health, field-based measurements of gait kinetics are desirable to assess free-living lower-extremity loading. The study aims were to determine correlations of peak vertical GRF and peak vertical LR with ankle peak vertical accelerations, and of peak resultant GRF and peak resultant LR with ankle peak resultant accelerations, and to compare them to correlations with tibia, thigh, and waist accelerations. GRF data were collected as ten healthy subjects (26 [19–34] years) performed 8–10 walking trials at velocities ranging from 0.19 to 3.05 m/s while wearing ankle, tibia, thigh, and waist accelerometers. While peak vertical accelerations of all locations were positively correlated with peak vertical GRF and LR (r2 > .53, P < .001), ankle peak vertical accelerations were the most correlated (r2 > .75, P < .001). All peak resultant accelerations were positively correlated with peak resultant GRF and LR (r2 > .57, P < .001), with waist peak resultant acceleration being the most correlated (r2 > .70, P < .001). The results suggest that ankle or waist accelerometers give the most accurate peak GRF and LR estimates and could be useful tools in relating physical activity to bone health.

This study aimed to define accelerations measured at the waist and lower extremities over a range of gait velocities to provide reference data for choosing the appropriate accelerometer for field-based human activity monitoring studies. Accelerations were measured with a custom activity monitor (± 16g) at the waist, thighs, and ankles in 11 participants over a range of gait velocities from slow walking to running speeds. The cumulative frequencies and peak accelerations were determined. Cumulative acceleration amplitudes for the waist, thighs, and ankles during gait velocities up to 4.8 m/s were within the standard commercial g-range (± 6g) in 99.8%, 99.0%, and 96.5% of the data, respectively. Conversely, peak acceleration amplitudes exceeding the limits of many commercially available activity monitors were observed at the waist, thighs, and ankles, with the highest peaks at the ankles, as expected. At the thighs, and more so at the ankles, nearly 50% of the peak accelerations would not be detected when the gait velocity exceeds a walking velocity. Activity monitor choice is application specific, and investigators should be aware that when measuring high-intensity gait velocity activities with commercial units that impose a ceiling at ± 6g, peak accelerations may not be measured.

Patient compliance is important when assessing movement, particularly in a free-living environment when patients are asked to don their own accelerometers. Reducing the number of accelerometers could increase patient compliance. The aims of this study were (1) to determine and compare the validity of different accelerometer combinations and placements for a previously developed posture and dynamic movement identification algorithm. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, right thigh, and waist of 12 healthy adults. Subjects performed a protocol in the laboratory including static orientations of standing, sitting, and lying down, and dynamic movements of walking, jogging, transitions between postures, and fidgeting to simulate free-living activity. When only one accelerometer was used, the thigh was found to be the optimal placement to identify both movement and static postures, with a misclassification error of 10%, and demonstrated the greatest accuracy for walking/fidgeting and jogging classification with sensitivities and positive predictive value (PPVs) greater than 93%. When two accelerometers were used, the waist-thigh accelerometers identified movement and static postures with greater accuracy than the thigh-ankle accelerometers (with a misclassification error of 11% compared to 17%). However, the thigh-ankle accelerometers demonstrated the greatest accuracy for walking/ fidgeting and jogging classification with sensitivities and PPVs greater than 93%. Movement can be accurately classified in healthy adults using tri-axial accelerometers placed on one or two of the following sites: waist, thigh, or ankle. Posture and transitions require an accelerometer placed on the waist and an accelerometer placed on the thigh.

Recording body accelerations has been investigated previously to monitor health, metabolic energy expenditure, postural sway and falls [1]. Activity monitoring and the subsequent analysis of the accelerometry data have also proven useful in detecting physical activity levels among Parkinson’s [2] and osteoarthritis patients [3].