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Back injuries and other occupational injuries are common in workers who engage in long, arduous physical labor. The risk of these injuries could be reduced using assistive devices that automatically detect an object lifting motion and support the user while they perform the lift; however, such devices must be able to detect the lifting motion as it occurs. We thus developed a system to detect the start and end of a lift (performed as a stoop or squat) in real time based on pelvic angle and the distance between the user's hands and the user's center of mass. The measurements were input to an algorithm that first searches for hand-center distance peaks in a sliding window, then checks the pelvic displacement angle to verify lift occurrence. The approach was tested with 5 participants, who performed a total of 100 lifts of four different types. The times of actual lifts were determined by manual video annotation. The median time error (absolute difference between detected and actual occurrence time) for lifts that were not false negatives was 0.11 s; a lift was considered a false negative if it was not detected within two seconds of it actually occurring. Furthermore, 95% of lifts that were detected occurred within 0.28 s of actual occurrence. This shows that it is possible to reliably detect lifts in real time based on the pelvic displacement angle and the distance between the user's hands and their center of mass. 

Trunk exoskeletons are wearable devices that support wearers during physically demanding tasks by reducing biomechanical loads and increasing stability. In this paper, we present a prototype sensorized passive trunk exoskeleton, which includes five motion processing units (3-axis accelerometers and gyroscopes with onboard digital processing), four one-axis flex sensors along the exoskeletal spinal column, and two one-axis force sensors for measuring the interaction force between the wearer and exoskeleton. A pilot evaluation of the exoskeleton was conducted with two wearers, who performed multiple everyday tasks (sitting on a chair and standing up, walking in a straight line, picking up a box with a straight back, picking up a box with a bent back, bending forward while standing, bending laterally while standing) while wearing the exoskeleton. Illustrative examples of the results are presented as graphs. Finally, potential applications of the sensorized exoskeleton as the basis for a semi-active exoskeleton design or for audio/haptic feedback to guide the wearer are discussed. 

Passive trunk exoskeletons support the human body with mechanical elements like springs and trunk compression, allowing them to guide motion and relieve the load on the spine. However, to provide appropriate support, elements of the exoskeleton (e.g., degree of compression) should be intelligently adapted to the current task. As it is not currently clear how adjusting different exoskeleton elements affects the wearer, this study preliminarily examines the effects of simultaneously adjusting both exoskeletal spinal column stiffness and trunk compression in a passive trunk exoskeleton. Six participants performed four dynamic tasks (walking, sit-to-stand, lifting a 20-lb box, lifting a 40-lb box) and experienced unexpected perturbations both without the exoskeleton and in six exoskeleton configurations corresponding to two compression levels and three stiffness levels. While results are preliminary due to the small sample size and relatively small increases in stiffness, they indicate that both compression and stiffness may affect kinematics and electromyography, that the effects may differ between activities, and that there may be interaction effects between stiffness and compression. As the next step, we will conduct a larger study with the same protocol more participants and larger stiffness increases to systematically evaluate the effects of different exoskeleton characteristics on the wearer.Clinical Relevance- Trunk exoskeletons can support wearers during a variety of different tasks, but their configuration may need to be intelligently adjusted to provide appropriate support. This pilot study provides information about the effects of exoskeleton back stiffness and trunk compression on the wearer, which can be used as a basis for more effective device design and usage. 

Lifting and carrying heavy objects is a major aspect of physically intensive jobs. Wearable sensors have previously been used to classify different ways of picking up an object, but have seen only limited use for automatic classification of load position and weight while a person is walking and carrying an object. In this proof-of-concept study, we thus used wearable inertial and electromyographic sensors for offline classification of different load positions (frontal vs. unilateral vs. bilateral side loads) and weights during gait. Ten participants performed 19 different carrying trials each while wearing the sensors, and data from these trials were used to train and evaluate classification algorithms based on supervised machine learning. The algorithms differentiated between frontal and other loads (side/none) with an accuracy of 100%, between frontal vs. unilateral side load vs. bilateral side load with an accuracy of 96.1%, and between different load asymmetry levels with accuracies of 75–79%. While the study is limited by a lack of electromyographic sensors on the arms and a limited number of load positions/weights, it shows that wearable sensors can differentiate between different load positions and weights during gait with high accuracy. In the future, such approaches could be used to control assistive devices or for long-term worker monitoring in physically demanding occupations. 

Although several studies have used wearable sensors to analyze human lifting, this has generally only been done in a limited manner. In this proof-of-concept study, we investigate multiple aspects of offline lift characterization using wearable inertial measurement sensors: detecting the start and end of the lift and classifying the vertical movement of the object, the posture used, the weight of the object, and the asymmetry involved. In addition, the lift duration, horizontal distance from the lifter to the object, the vertical displacement of the object, and the asymmetric angle are computed as lift parameters. Twenty-four healthy participants performed two repetitions of 30 different main lifts each while wearing a commercial inertial measurement system. The data from these trials were used to develop, train, and evaluate the lift characterization algorithms presented. The lift detection algorithm had a start time error of 0.10 s ± 0.21 s and an end time error of 0.36 s ± 0.27 s across all 1489 lift trials with no missed lifts. For posture, asymmetry, vertical movement, and weight, our classifiers achieved accuracies of 96.8%, 98.3%, 97.3%, and 64.2%, respectively, for automatically detected lifts. The vertical height and displacement estimates were, on average, within 25 cm of the reference values. The horizontal distances measured for some lifts were quite different than expected (up to 14.5 cm), but were very consistent. Estimated asymmetry angles were similarly precise. In the future, these proof-of-concept offline algorithms can be expanded and improved to work in real-time. This would enable their use in applications such as real-time health monitoring and feedback for assistive devices. 

Abstract The science and technology of wearable robots are steadily advancing, and the use of such robots in our everyday life appears to be within reach. Nevertheless, widespread adoption of wearable robots should not be taken for granted, especially since many recent attempts to bring them to real-life applications resulted in mixed outcomes. The aim of this article is to address the current challenges that are limiting the application and wider adoption of wearable robots that are typically worn over the human body. We categorized the challenges into mechanical layout, actuation, sensing, body interface, control, human–robot interfacing and coadaptation, and benchmarking. For each category, we discuss specific challenges and the rationale for why solving them is important, followed by an overview of relevant recent works. We conclude with an opinion that summarizes possible solutions that could contribute to the wider adoption of wearable robots. 

Competitive rehabilitation games can enhance motivation and exercise intensity compared to solo exercise; however, since such games may be played by two people with different abilities, game difficulty must be dynamically adapted to suit both players. State-of-the-art adaptation algorithms are based on players' performance (e.g., score), which may not be representative of the patient's physical and psychological state. Instead, we propose a method that estimates players' states in a competitive game based on the covariation of players' physiological responses. The method was evaluated in 10 unimpaired pairs, who played a competitive game in 6 conditions while 5 physiological responses were measured: respiration, skin conductance, heart rate, and 2 facial electromyograms. Two physiological linkage methods were used to assess the similarity of the players' physiological measurements: coherence of raw measurements and correlation of heart and respiration rates. These linkage features were compared to traditional individual physiological features in classification of players' affects (enjoyment, valence, arousal, perceived difficulty) into 'low' and 'high' classes. Classifiers based on physiological linkage resulted in higher accuracies than those based on individual physiological features, and combining both feature types yielded the highest classification accuracies (75% to 91%). These classifiers will next be used to dynamically adapt game difficulty during rehabilitation.