Design and Pilot Evaluation of a Prototype Sensorized Trunk Exoskeleton.

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.

Simultaneously varying back stiffness and trunk compression in a passive trunk exoskeleton during different activities: A pilot study.

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.

Human-Human Connected Dyads Learning a Visuomotor Rotation in a Targeted Reaching Task.

Little is known about how two people physically coupled together (a dyad) can accomplish tasks. In a pilot study we tested how healthy inexperienced and experienced dyads learn to repeatedly reach to a target and stop while challenged with a 30 degree visuomotor rotation. We employed the Pantograph investigational device that haptically couples partners movements while providing cursor feedback, and we measured the amount and speed of learning to test a prevailing hypothesis: dyads with no experience learn faster than an experienced person coupled with a novice. We found significant straightening of movements for dyads in terms of amount of learning (2.662±0.102 cm and 2.576±0.024 cm for the novice-novice and novice-experienced groups) at rapid rates (time constants of 17.83 ± 2.85 and 18.17.17±6.72 movements), which was nearly half the learning time as solo individuals' studies. However, we found no differences between the novice-novice and experienced-novice groups, though retrospectively our power was only 3 percent. This pilot study demonstrates new opportunities to investigate the advantages of partner-facilitated learning with solely haptic communication which and can lead to insights on control in human physical interactions and can guide the design of future human-robot-human interaction systems.

Toward Real-Time Detection of Object Lifting Using Wearable Inertial Measurement Units.

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.

Automatic Estimation of Interpersonal Engagement During Naturalistic Conversation Using Dyadic Physiological Measurements.

Physiological responses of two interacting individuals contain a wealth of information about the dyad: for example, the degree of engagement or trust. However, nearly all studies on dyadic physiological responses have targeted group-level analysis: e.g., correlating physiology and engagement in a large sample. Conversely, this paper presents a study where physiological measurements are combined with machine learning algorithms to dynamically estimate the engagement of individual dyads. Sixteen dyads completed 15-min naturalistic conversations and self-reported their engagement on a visual analog scale every 60 s. Four physiological signals (electrocardiography, skin conductance, respiration, skin temperature) were recorded, and both individual physiological features (e.g., each participant's heart rate) and synchrony features (indicating degree of physiological similarity between two participants) were extracted. Multiple regression algorithms were used to estimate self-reported engagement based on physiological features using either leave-interval-out crossvalidation (training on 14 60-s intervals from a dyad and testing on the 15th interval from the same dyad) or leave-dyad-out crossvalidation (training on 15 dyads and testing on the 16th). In leave-interval-out crossvalidation, the regression algorithms achieved accuracy similar to a 'baseline' estimator that simply took the median engagement of the other 14 intervals. In leave-dyad-out crossvalidation, machine learning achieved a slightly higher accuracy than the baseline estimator and higher accuracy than an independent human observer. Secondary analyses showed that removing synchrony features and personality characteristics from the input dataset negatively impacted estimation accuracy and that engagement estimation error was correlated with personality traits. Results demonstrate the feasibility of dynamically estimating interpersonal engagement during naturalistic conversation using physiological measurements, which has potential applications in both conversation monitoring and conversation enhancement. However, as many of our estimation errors are difficult to contextualize, further work is needed to determine acceptable estimation accuracies.

Evaluation of the HeroWear Apex back-assist exosuit during multiple brief tasks.

Trunk exoskeletons are wearable devices that support humans during physically demanding tasks by reducing biomechanical loads on the back. While most trunk exoskeletons are rigid devices, more lightweight soft exoskeletons (exosuits) have recently been developed. One such exosuit is the HeroWear Apex, which achieved promising results in the developers' own work but has not been independently evaluated. This paper thus presents an evaluation of the Apex with 20 adult participants during multiple brief tasks: standing up from a stool with a symmetric or asymmetric load, lifting a unilateral or bilateral load from the floor to waist level, lifting the same bilateral load with a 90-degree turn to the right, lowering a bilateral load from waist level to floor, and walking while carrying a bilateral load. The tasks were performed in an ABA-style protocol: first with exosuit assistance disengaged, then with it engaged, then disengaged again. Four measurement types were taken: electromyography (of the erector spinae, rectus abdominis, and middle trapezius), trunk kinematics, self-report ratings, and heart rate. The exosuit decreased the erector spinae electromyogram by about 15% during object lifting and lowering tasks; furthermore, participants found the exosuit mildly to moderately helpful. No adverse effects on other muscles or during non-lifting tasks were noted, and a decrease in middle trapezius electromyogram was observed for one task. This confirms that the HeroWear Apex could reduce muscle demand and fatigue. The results may transfer to other exoskeletons with similar design principles, and may inform researchers working with other wearable devices.

Automated affect classification and task difficulty adaptation in a competitive scenario based on physiological linkage: An exploratory study.

In competitive and cooperative scenarios, task difficulty should be dynamically adapted to suit people with different abilities. State-of-the-art difficulty adaptation methods for such scenarios are based on task performance, which conveys little information about user-specific factors such as workload. Thus, we present an exploratory study of automated affect recognition and task difficulty adaptation in a competitive scenario based on physiological linkage (covariation of participants' physiological responses). Classification algorithms were developed in an open-loop study where 16 pairs played a competitive game while 5 physiological responses were measured: respiration, skin conductance, electrocardiogram, and 2 facial electromyograms. Physiological and performance data were used to classify four self-reported variables (enjoyment, valence, arousal, perceived difficulty) into two or three classes. The highest classification accuracies were obtained for perceived difficulty: 84.3% for two-class and 60.5% for three-class classification. As a proof of concept, the developed classifiers were used in a small closed-loop study to dynamically adapt game difficulty. While this closed-loop study found no clear advantages of physiology-based adaptation, it demonstrated the technical feasibility of such real-time adaptation. In the long term, physiology-based task adaptation could enhance competition and cooperation in many multi-user settings (e.g., education, manufacturing, exercise).

User Experience With Dynamic Difficulty Adjustment Methods for an Affective Exergame: Comparative Laboratory-Based Study.

BACKGROUND: In affective exergames, game difficulty is dynamically adjusted to match the user's physical and psychological state. Such an adjustment is commonly made based on a combination of performance measures (eg, in-game scores) and physiological measurements, which provide insight into the player's psychological state. However, although many prototypes of affective games have been presented and many studies have shown that physiological measurements allow more accurate classification of the player's psychological state than performance measures, few studies have examined whether dynamic difficulty adjustment (DDA) based on physiological measurements (which requires additional sensors) results in a better user experience than performance-based DDA or manual difficulty adjustment. OBJECTIVE: This study aims to compare five DDA methods in an affective exergame: manual (player-controlled), random, performance-based, personality-performance-based, and physiology-personality-performance-based (all-data). METHODS: A total of 50 participants (N=50) were divided into five groups, corresponding to the five DDA methods. They played an exergame version of Pong for 18 minutes, starting at a medium difficulty; every 2 minutes, two game difficulty parameters (ball speed and paddle size) were adjusted using the participant's assigned DDA method. The DDA rules for the performance-based, personality-performance-based, and all-data groups were developed based on data from a previous open-loop study. Seven physiological responses were recorded throughout the sessions, and participants self-reported their preferred changes to difficulty every 2 minutes. After playing the game, participants reported their in-game experience using two questionnaires: the Intrinsic Motivation Inventory and the Flow Experience Measure. RESULTS: Although the all-data method resulted in the most accurate changes to ball speed and paddle size (defined as the percentage match between DDA choice and participants' preference), no significant differences between DDA methods were found on the Intrinsic Motivation Inventory and Flow Experience Measure. When the data from all four automated DDA methods were pooled together, the accuracy of changes in ball speed was significantly correlated with players' enjoyment (r=0.38) and pressure (r=0.43). CONCLUSIONS: Although our study is limited by the use of a between-subjects design and may not generalize to other exergame designs, the results do not currently support the inclusion of physiological measurements in affective exergames, as they did not result in an improved user experience. As the accuracy of difficulty changes is correlated with user experience, the results support the development of more effective DDA methods. However, they show that the inclusion of physiological measurements does not guarantee a better user experience even if it yields promising results in offline cross-validation.

Challenges and solutions for application and wider adoption of wearable robots.

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.

Biomechanical comparisons of back and front squats with a straight bar and four squats with a transformer bar.

The purpose was to quantify trunk and lower extremity biomechanics among back and front squats with a straight bar and four squats with different anterior-posterior load placements imposed by a transformer bar. Ten males and eight females performed six squat conditions: back and front squats with a straight bar, back and front squats with a transformer bar, and squats with more posteriorly or anteriorly placed loads with a transformer bar. A constant load of 70% of the participant's one-repetition maximum in the straight-bar front squat was used. Kinematic and kinetic data were collected to quantify joint biomechanics at an estimated parallel squat position in the descending and ascending phases. Squats with more anteriorly placed load significantly decreased trunk flexion and pelvis anterior tilt angles with large effect sizes but increased low-back extension moments with medium to large effect sizes. Hip, knee, and ankle extension moments were generally similar among most conditions. Participants adjusted their trunk and pelvis to mediate the effects of load placements on low-back and lower extremity moments. While lower extremity loading was similar among different squats, the different trunk and pelvis angles and low-back moments should be taken into consideration for people with low-back impairment.

Haptic Coupling in Dyads Improves Motor Learning in a Simple Force Field.

In dyadic motor learning, pairs of people learn the same motion while their limbs are loosely coupled together using haptic devices. Such coupled learning has been shown to outperform solo learning (including robot-guided learning) for simple one-degree-of-freedom tasks. However, results from more complex tasks are limited and sometimes conflicting. We thus evaluated coupled learning in a two-degree-of-freedom tracking task where participants also had to compensate for a simple force field. Participant pairs were split into two groups: an experiment group that experienced a compliant haptic coupling between participants' hands and a control group that did not. The study protocol consisted of 70 repetitions of 18.9-second tracking trials: 10 initial solo trials with no coupling, 50 "learning" trials (where participants in the experiment group were coupled), and 10 final solo trials with no coupling. The experiment group (coupled) improved their solo tracking performance both in the presence (p = 0.008) and absence (p <; 0.001) of the force field; however, the control group (no coupling) only improved their solo performance in the absence of the force field (p <; 0.001) but not in the presence of the field (p = 0.81). This suggests that dyadic motor learning can outperform solo learning for two-dimensional tracking motions in the presence of a simple force field, though the mechanism by which learning is improved is not yet clear.Clinical Relevance-As motor learning is critical for applications such as motor rehabilitation, dyadic training could be used to achieve a better overall outcome and a faster learning speed in these applications compared to solo training.

Sensor Fusion in Assistive and Rehabilitation Robotics.

As the world's population gradually grows older, more and more adults are experiencing sensory-motor disabilities due to stroke, traumatic brain injury, spinal cord injury and other diseases [...].

Corrections to "A Pilot Study of Varying Thoracic and Abdominal Compression in a Reconfigurable Trunk Exoskeleton During Different Activities" [Jun 20 1585-1594].

Presents a corrected version of Table A-II in the above named paper.

Load Position and Weight Classification during Carrying Gait Using Wearable Inertial and Electromyographic Sensors.

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.

Characterizing Human Box-Lifting Behavior Using Wearable Inertial Motion Sensors.

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.

Effects of Different Opponent Types on Motivation and Exercise Intensity in a Competitive Arm Exercise Game.

Objective: Competitive exercise games are popular in areas like rehabilitation and weight loss due to their positive effects on motivation. However, it is unclear whether a human opponent is necessary, as the same benefits may be achievable with a "human-like" computer-controlled opponent or a human who talks to the player without playing the game. Our objective was to compare four opponent types in a competitive exercise game: a simple computer opponent, "human-like" computer opponent, human opponent, and a simple computer opponent accompanied by a player-selected human who chats with the player. Materials and Methods: Sixteen participants (3 women, 24.4 ± 7.7 years old) played a competitive arm exercise game in the above four conditions. Exercise intensity was measured with inertial sensors, and four motivation scales were measured with the Intrinsic Motivation Inventory. After playing, participants answered several questions regarding their preferences. Results: The human opponent was the favorite for 14 of 16 participants and resulted in the highest interest/enjoyment and exercise intensity. All participants preferred the human opponent over the computer opponent accompanied by a human companion. Finally, 12 of 16 participants preferred the "human-like" computer opponent over the simple one. Conclusion: Our results have two implications for competitive exercise games. First, they indicate that developing computer-controlled opponents with more human-like behavior is worthwhile, but that the best results are achieved with human opponents. Second, social interaction without in-game interaction does not provide an enjoyable, intense experience. However, our results should be verified with different target populations for exercise games.

A Pilot Study of Varying Thoracic and Abdominal Compression in a Reconfigurable Trunk Exoskeleton During Different Activities.

OBJECTIVE: Trunk exoskeletons are a new technology with great promise for human rehabilitation, assistance and augmentation. However, it is unclear how different exoskeleton features affect the wearer's body during different activities. This study thus examined how varying a trunk exoskeleton's thoracic and abdominal compression affects trunk kinematics and muscle demand during several activities. METHODS: We developed a trunk exoskeleton that allows thoracic and abdominal compression to be changed quickly and independently. To evaluate the effect of varying compression, 12 participants took part in a two-session study. In the first session, they performed three activities (walking, sit-to-stand, lifting a box). In the second session, they experienced unexpected perturbations while sitting. This was done both without the exoskeleton and in four exoskeleton configurations with different thoracic and abdominal compression levels. Trunk flexion angle, low back extension moment and the electromyogram of the erector spinae and rectus abdominis were measured in both sessions. RESULTS: Different exoskeleton compression levels resulted in significantly different peak trunk flexion angles and peak electromyograms of the erector spinae. However, the effects of compression differed significantly between activities. CONCLUSION: Our results indicate that a trunk exoskeleton's thoracic and abdominal compression affect the wearer's kinematics and muscle demand; furthermore, a single compression configuration is not appropriate for all activities. SIGNIFICANCE: The study suggests that future trunk exoskeletons should either be able to vary their compression levels to suit different activities or should have the compression designed for a specific activity in order to be beneficial to the wearer.

Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics.

Human psychological (cognitive and affective) dimensions can be assessed using several methods, such as physiological or performance measurements. To date, however, few studies have compared different data modalities with regard to their ability to enable accurate classification of different psychological dimensions. This study thus compares classification accuracies for four psychological dimensions and two subjective preferences about computer game difficulty using three data modalities: physiology, performance, and personality characteristics. Thirty participants played a computer game at nine difficulty configurations that were implemented via two difficulty parameters. In each configuration, seven physiological measurements and two performance variables were recorded. A short questionnaire was filled out to assess the perceived difficulty, enjoyment, valence, arousal, and the way the participant would like to modify the two difficulty parameters. Furthermore, participants' personality characteristics were assessed using four questionnaires. All combinations of the three data modalities (physiology, performance, and personality) were used to classify six dimensions of the short questionnaire into either two, three or many classes using four classifier types: linear discriminant analysis, support vector machine (SVM), ensemble decision tree, and multiple linear regression. The classification accuracy varied widely between the different psychological dimensions; the highest accuracies for two-class and three-class classification were 97.6 and 84.1%, respectively. Normalized physiological measurements were the most informative data modality, though current game difficulty, personality and performance also contributed to classification accuracy; the best selected features are presented and discussed in the text. The SVM and multiple linear regression were the most accurate classifiers, with regression being more effective for normalized physiological data. In the future, we will further examine the effect of different classification approaches on user experience by detecting the user's psychological state and adapting game difficulty in real-time. This will allow us to obtain a complete picture of the performance of affect-aware systems in both an offline (classification accuracy) and real-time (effect on user experience) fashion.

Toward real-world evaluations of trunk exoskeletons using inertial measurement units.

Trunk exoskeletons are an emerging technology that could reduce spinal loading, guide trunk motion, and augment lifting ability. However, while they have achieved promising results in brief laboratory studies, they have not yet been tested in longer-term real-world studies - partially due to reliance on stationary sensors such as cameras. To enable future real-world evaluations of trunk exoskeletons, this paper describes two preliminary studies on using inertial measurement units (IMUs) to collect kinematic data from an exoskeleton wearer. In the first study, a participant performed three activities (walking, sit-to-stand, box lifting) while trunk flexion angle was measured with both IMUs and reference cameras. The mean absolute difference in flexion angle between the two methods was 1.4° during walking, 3.6° during sit-to-stand and 5.2° during box lifting, showing that IMUs can measure trunk flexion with a reasonable accuracy. In the second study, six participants performed five activities (standing, sitting straight, slouching, 'good' lifting, 'bad' lifting), and a naïve Bayes classifier was used to automatically classify the activity from IMU data. The classification accuracy was 92.2%, indicating the feasibility of automated activity classification using IMUs. The IMUs will next be used to obtain longer-term recordings of different activities performed both with and without a trunk exoskeleton to determine how the exoskeleton affects a person's posture and behavior.

Pilot Long-term Evaluation of Competitive and Cooperative Exercise Games in Inpatient Stroke Rehabilitation.

Interpersonal rehabilitation games, which allow patients to compete or cooperate with other patients or unimpaired loved ones, have demonstrated promising short-term results, but have not yet been tested in longer-term studies. This paper thus presents a preliminary 9-session evaluation of interpersonal rehabilitation games for post-stroke arm exercise. Two pairs of stroke survivors were provided with a system that included one competitive and one cooperative rehabilitation game, and exercised with it for 9 sessions in addition to their conventional therapy. They were able to choose the game they wanted to play in each session, and had to exercise for at least 10 minutes per session. Both pairs completed the protocol without any issues, reporting high levels of motivation and consistent levels of exercise intensity (measured using inertial sensors) across the sessions. Furthermore, the maximum difficulty levels reached in the cooperative game increased over time, and improvements of 1-8 points were observed on the Box and Block test. These results indicate that 2 different interpersonal games are sufficient to promote high levels of motivation and exercise intensity for 9 sessions performed over a 3-week period. As the next step, our system will be expanded with additional competitive, cooperative and single-player games, then tested in full clinical trials in both clinical and home environments.