Objective: This study aimed to determine the effects of active workstation designs on speed and error during typing, mouse pointing, and combined type and mouse-use tasks. Background: Office ergonomics has focused on musculoskeletal disorder prevention; however, increasing computer-based work also increases health risks associated with inactivity. Workstations allowing computer users to walk or cycle while performing computer tasks have been shown to demand sufficient energy expenditure to result in significant health benefits. However the performance effects of being active while using a computer have not been documented. Method: Thirty office workers (16 female, 15 touch typists) performed standardized computer tasks in six workstation conditions: sitting, standing, walking at 1.6 km/h and 3.2 km/h, and cycling at 5 and 30 watts. Performance, perceived performance, and heart rate were measured. Results: Computer task performance was lower when walking and slightly lower when cycling, compared with chair sitting. Standing performance was not different from sitting performance. Mouse performance was more affected than typing performance. Performance decrements were equal for females and males and for touch typists and nontouch typists. Conclusion: Performance decrements maybe related to both biomechanical and cognitive processes. Active workstations may be less suitable for mouse-intensive work and susceptible users. Application: Although active workstations may result in some decrement in performance, their ability to increase daily energy expenditure may make them a feasible solution for workplace inactivity.

Wearable sensors are portable measurement tools that are becoming increasingly popular for the measurement of joint angle in the upper limb. With many brands emerging on the market, each with variations in hardware and protocols, evidence to inform selection and application is needed. Therefore, the objectives of this review were related to the use of wearable sensors to calculate upper limb joint angle. We aimed to describe (i) the characteristics of commercial and custom wearable sensors, (ii) the populations for whom researchers have adopted wearable sensors, and (iii) their established psychometric properties. A systematic review of literature was undertaken using the following data bases: MEDLINE, EMBASE, CINAHL, Web of Science, SPORTDiscus, IEEE, and Scopus. Studies were eligible if they met the following criteria: (i) involved humans and/or robotic devices, (ii) involved the application or simulation of wearable sensors on the upper limb, and (iii) calculated a joint angle. Of 2191 records identified, 66 met the inclusion criteria. Eight studies compared wearable sensors to a robotic device and 22 studies compared to a motion analysis system. Commercial (n = 13) and custom (n = 7) wearable sensors were identified, each with variations in placement, calibration methods, and fusion algorithms, which were demonstrated to influence accuracy. Wearable sensors have potential as viable instruments for measurement of joint angle in the upper limb during active movement. Currently, customised application (i.e. calibration and angle calculation methods) is required to achieve sufficient accuracy (error < 5°). Additional research and standardisation is required to guide clinical application. This systematic review was registered with PROSPERO ( CRD42017059935 ).

Deep learning models developed to predict knee joint kinematics are usually trained on inertial measurement unit (IMU) data from healthy people and only for the activity of walking. Yet, people with knee osteoarthritis have difficulties with other activities and there are a lack of studies using IMU training data from this population. Our objective was to conduct a proof-of-concept study to determine the feasibility of using IMU training data from people with knee osteoarthritis performing multiple clinically important activities to predict knee joint sagittal plane kinematics using a deep learning approach. We trained a bidirectional long short-term memory model on IMU data from 17 participants with knee osteoarthritis to estimate knee joint flexion kinematics for phases of walking, transitioning to and from a chair, and negotiating stairs. We tested two models, a double-leg model (four IMUs) and a single-leg model (two IMUs). The single-leg model demonstrated less prediction error compared to the double-leg model. Across the different activity phases, RMSE (SD) ranged from 7.04° (2.6) to 11.78° (6.04), MAE (SD) from 5.99° (2.34) to 10.37° (5.44), and Pearson’s R from 0.85 to 0.99 using leave-one-subject-out cross-validation. This study demonstrates the feasibility of using IMU training data from people who have knee osteoarthritis for the prediction of kinematics for multiple clinically relevant activities.

The link between static and dynamic landing lumbar postures, when gymnasts are exposed to large ground reaction forces, has not been established. This investigation aimed to (a) determine if a relationship exists between sagittal static and dynamic landing lumbar spine angles at peak ground reaction force (GRF) and (b) quantify how close to end-range postures the gymnasts were at landing peak GRF. Twenty-one female gymnasts’ upper and lower lumbar spine angles were recorded: statically in sitting and standing, during landing of three gymnastic skills, and during active end-range lumbar flexion. Pearson’s correlations were used to investigate relationships between the angles in different postures. Significant correlations (r = .77–.89, p < .01) were found between all the static/dynamic postures in the lower lumbar spine angle, while fewer and less significant upper lumbar spine correlations were reported. Thirty percent of gymnasts landed a backsault with their lower lumbar spine flexed beyond their active end-range while experiencing GRF 6.8–13.3 times their body weight. These results inform low back pain prevention and management strategies in this population and highlight areas for future research.

This study aimed to compare thumb kinematics and upper limb muscle activity, and the influence of hand size, when texting on a keypad smartphone and a touchscreen smartphone. Furthermore, the study compared exertion, discomfort, and performance when texting on the two phones. The thumb kinematics were tracked using a 3D motion analysis system and muscle activity was registered in six upper limb muscles using surface electromyography in 19 participants. When texting on the touchscreen phone compared to the keypad phone thumb flexion (p = 0.008) and flexion/extension range of motion were smaller (p = 0.02), the thumb was on average less internally rotated (p = 0.02), and activity (50th and 90th percentile) of the thumb and forearm muscles was lower (p ≤ 0.05). The differences in thumb flexion were found only in the group with shorter hands and the differences in muscle activity was found only in the group with longer hands. These findings suggest there are differences in risks for developing musculoskeletal disorders during smartphone use with different key activation mechanisms and different hand sizes.