Christian Werner

Human sit-to-stand transfer modeling towards intuitive and biologically-inspired robot assistance

Sit-to-stand (STS) transfers are a common human task which involves complex sensorimotor processes to control the highly nonlinear musculoskeletal system. In this paper, typical unassisted and assisted human STS transfers are formulated as optimal feedback control problem that finds a compromise between task end-point accuracy, human balance, energy consumption, smoothness of motion and control and takes further human biomechanical control constraints into account. Differential dynamic programming is employed, which allows taking the full, nonlinear human dynamics into consideration. The biomechanical dynamics of the human is modeled by a six link rigid body including leg, trunk and arm segments. Accuracy of the proposed modelling approach is evaluated for different human healthy and patient/elderly subjects by comparing simulations and experimentally collected data. Acceptable model accuracy is achieved with a generic set of constant weights that prioritize the different criteria. Finally, the proposed STS model is used to determine optimal assistive strategies suitable for either a person with specific body segment weakness or a more general weakness. These strategies are implemented on a robotic mobility assistant and are intensively evaluated by 33 elderlies, mostly not able to perform unassisted STS transfers. The validation results show a promising STS transfer success rate and overall user satisfaction.

An Integrated Decision Making Approach for Adaptive Shared Control of Mobility Assistance Robots

Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots.

Experimental validation of human pathological gait analysis for an assisted living intelligent robotic walker

A robust and effective gait analysis functionality is an essential characteristic for an assistance mobility robot dealing with elderly persons. The aforementioned functionality is crucial for dealing with mobility disabilities which are widespread in these parts of the population. In this work we present experimental validation of our in house developed system. We are using real data, collected from an ensemble of different elderly persons with a number of pathologies, and we present a validation study by using a GaitRite System. Our system, following the standard literature conventions, characterizes the human motion with a set of parameters which subsequently can be used to assess and distinguish between possible motion disabilities, using a laser range finder as its main sensor. The initial results, presented in this work, demonstrate the applicability of our framework in real test cases. Regarding such frameworks, a crucial technical question is the necessary complexity of the overall tracking system. To answer this question, we compare two approaches with different complexity levels. The first is a static rule based system acting on filtered laser data, while the second system utilizes a Hidden Markov Model for gait cycle estimation, and extraction of the gait parameters. The results demonstrate that the added complexity of the HMM system is necessary for improving the accuracy and efficacy of the system.

Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision

We explore new directions for automatic human gesture recognition and human joint angle estimation as applied for human-robot interaction in the context of an actual challenging task of assistive living for real-life elderly subjects. Our contributions include state-of-the-art approaches for both low- and mid-level vision, as well as for higher level action and gesture recognition. The first direction investigates a deep learning based framework for the challenging task of human joint angle estimation on noisy real world RGB-D images. The second direction includes the employment of dense trajectory features for online processing of videos for automatic gesture recognition with real-time performance. Our approaches are evaluated both qualitative and quantitatively on a newly acquired dataset that is constructed on a challenging real-life scenario on assistive living for elderly subjects.

Evaluation Studies of Robotic Rollators by the User Perspective: A Systematic Review

Background: Robotic rollators enhance the basic functions of established devices by technically advanced physical, cognitive, or sensory support to increase autonomy in persons with severe impairment. In the evaluation of such ambient assisted living solutions, both the technical and user perspectives are important to prove usability, effectiveness and safety, and to ensure adequate device application. Objective: The aim of this systematic review is to summarize the methodology of studies evaluating robotic rollators with focus on the user perspective and to give recommendations for future evaluation studies. Methods: A systematic literature search up to December 31, 2014, was conducted based on the Cochrane Review methodology using the electronic databases PubMed and IEEE Xplore. Articles were selected according to the following inclusion criteria: evaluation studies of robotic rollators documenting human-robot interaction, no case reports, published in English language. Results: Twenty-eight studies were identified that met the predefined inclusion criteria. Large heterogeneity in the definitions of the target user group, study populations, study designs and assessment methods was found across the included studies. No generic methodology to evaluate robotic rollators could be identified. We found major methodological shortcomings related to insufficient sample descriptions and sample sizes, and lack of appropriate, standardized and validated assessment methods. Long-term use in habitual environment was also not evaluated. Conclusions: Apart from the heterogeneity, methodological deficits in most of the identified studies became apparent. Recommendations for future evaluation studies include: clear definition of target user group, adequate selection of subjects, inclusion of other assistive mobility devices for comparison, evaluation of the habitual use of advanced prototypes, adequate assessment strategy with established, standardized and validated methods, and statistical analysis of study results. Assessment strategies may additionally focus on specific functionalities of the robotic rollators allowing an individually tailored assessment of innovative features to document their added value.

Experimental comparison of human gait tracking algorithms: Towards a context-aware mobility assistance robotic walker

Towards a mobility assistance robot for the elderly, it is essential to develop a robust and accurate gait tracking system. Various pathologies cause mobility inabilities to the aged population, leading to different gait patterns and walking speed. In this work, we present the experimental comparison of two user leg tracking systems of a robotic assistance walker, using data collected by a laser range sensor. The first one is a Kalman Filter tracking system, while the second one proposes the use of Particle Filters. The tracking systems provide the positions and velocities of the users legs, which are used as observations into an HMM-based gait phases recognition system. The spatiotemporal results of the HMM framework are employed for computing parameters that characterize the human motion, which subsequently can be used to assess and distinguish between possible motion disabilities. For the experimental comparison, we are using real data collected from an ensemble of different elderly persons with a number of pathologies, and ground truth data from a GaitRite System. The results presented in this work, demonstrate the applicability of the tracking systems in real test cases.

Using Augmented Reality and Gamification to Empower Rehabilitation Activities and Elderly Persons. A Study Applying Design Thinking

We present the design of a system combining augmented reality (AR) and gamification to support elderly persons’ rehabilitation activities. The system is attached to the waist; it collects detailed movement data and at the same time augments the user’s path by projections. The projected AR-elements can provide location-based information or incite movement games. The collected data can be observed by therapists. Based on this data, the challenge level can be more frequently adapted, keeping up the patient’s motivation. The exercises can involve cognitive elements (for mild cognitive impairments), physiological elements (rehabilitation), or both. The overall vision is an individualized and gamified therapy. Thus, the system also offers application scenarios beyond rehabilitation in sports. In accordance with the methodology of design thinking, we present a first specification and a design vision based on inputs from business experts, gerontologists, physiologists, psychologists, game designers, cognitive scientists and computer scientists.

A systematic review of study results reported for the evaluation of robotic rollators from the perspective of users

Abstract Purpose: To evaluate the effectiveness and perception of robotic rollators (RRs) from the perspective of users. Methods: Studies identified in a previous systematic review published on 2016 on the methodology of studies evaluating RRs by the user perspective were re-screened for eligibility based on the following inclusion criteria: evaluation of the human–robot interaction from the user perspective, use of standardized outcome measurements, and quantitative presentation of study results. Results: Seventeen studies were eligible for inclusion. Due to the clinical and methodological heterogeneity across studies, a narrative synthesis of study results was conducted. We found conflicting results concerning the effectiveness of the robotic functionalities of the RRs. Only a few studies reported superior user performance or reduced physical demands with the RRs compared to unassisted conditions or conventional assistive mobility devices; however, without providing statistical evidence. The user perception of the RRs was found to be generally positive. Conclusions: There is still no sufficient evidence on the effectiveness of RRs from the user perspective. More well-designed, high-quality studies with adequate study populations, larger sample sizes, appropriate assessment strategies with outcomes specifically tailored to the robotic functionalities, and statistical analyses of results are required to evaluate RRs at a higher level of evidence. Implications for Rehabilitation RRs cover intelligent functionalities that focus on gait assistance, obstacle avoidance, navigation assistance, sit-to-stand transfer, body weight support or fall prevention. The evaluation from the user perspective is essential to ensure that RRs effectively address users’ needs, requirements and preferences. The evidence on the effectiveness of RRs is severely hampered by the low methodological quality of most of the available studies. RRs seem generally to be perceived as positive by the users. There is very limited evidence on the effectiveness and benefits of RRs compared to conventional assistive mobility devices. Further research with high methodological quality needs to be conducted to reach more robust conclusions about the effectiveness of RRs.

Motor-cognitive effects of a computerized game-based training method in people with dementia: a randomized controlled trial

ABSTRACT Objectives: To examine the effects of a computerized, game-based training on motor-cognitive performances, the transfer of training effects on untrained tasks, and the sustainability of training gains in people with dementia. Method: Ninety-nine individuals with a mean age of 82.9 (5.8) and dementia participated in a 10-week randomized controlled trial with three-month follow-up. The intervention group (IG) received a motor-cognitive training on (Physiomat®) including concurrent dual-tasks of balance control with cognitive demands (Physiomat®-Trail Making Tasks (PTMTs)). The control group (CG) performed non-specific, low-intensity exercises. Duration and accuracy at different complexity levels of trained and untrained PTMTs and the number of successfully performed tasks (PTMT score) were assessed. Results: Physiomat® training significantly improved the duration and accuracy at almost all complexity levels of trained (P ≤ 0.001–0.047, ηp2 = 0.065–0.589) and untrained PTMTs (P < 0.001–0.005, ηp2 = 0.073–0.459). Significant effects were also found for the PTMT score of trained (P < 0.001, ηp2 = 0.211) and untrained PTMTs (P < 0.001, ηp2 = 0.184). Training gains were partly sustained at follow-up. Conclusion: Physiomat® is feasible and has the potential to sustainably improve motor-cognitive performances in people with dementia.

Contents Vol. 62, 2016

Clinical Section D. Aarsland, Stockholm J. Attems, Newcastle upon Tyne M. Burtscher, Innsbruck G. Del Giudice, Siena V.C. Emery, Guildford J.D. Erusalimsky, Cardiff L. Fontana, St. Louis, Mo. J.J. Goronzy, Stanford, Calif. U. Granacher, Potsdam K. Hauer, Heidelberg F. Kronenberg, Innsbruck T.F. Lue, San Francisco, Calif. A.B. Maier, Parkville, Vic. J. Olshansky, Chicago, Ill. A. Stuck, Bern T.M. Stulnig, Vienna J. Tao, Guangzhou D.C. Willcox, Ginowan D. Ziegler, Düsseldorf