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Background: Behavioral observations support clinical in-depth phenotyping but phenotyping and pattern recognition are affected by training background. As Attention Deficit Hyperactivity Disorder, Restless Legs syndrome/Willis Ekbom disease and medication induced activation syndromes (including increased irritability and/or akathisia), present with hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors), we first developed a non-interpretative, neutral pictogram-guided phenotyping language (PG-PL) for describing body-segment movements during sitting., Methodology & Results: The PG-PL was applied for annotating 12 1-min sitting-videos (inter-observer agreements >85%->97%) and these manual annotations were used as a ground truth to develop an automated algorithm using OpenPose, which locates skeletal landmarks in 2D video. We evaluated the algorithm's performance against the ground truth by computing the area under the receiver operator curve (>0.79 for the legs, arms, and feet, but 0.65 for the head). While our pixel displacement algorithm performed well for the legs, arms, and feet, it predicted head motion less well, indicating the need for further investigations., Conclusion: This first automated analysis algorithm allows to start the discussion about distinct phenotypical characteristics of H-behaviors during structured behavioral observations and may support differential diagnostic considerations via in-depth phenotyping of sitting behaviors and, in consequence, of better treatment concepts., (Copyright © 2020. Published by Elsevier Ltd.)

Lower limb exoskeletons (LLEs) are susceptible to falls, and users are at risk of head and/or hip injuries. To address concerns regarding the safety of LLE users, optimization techniques were used to study safe-fall control strategies. Simulation results of these studies showed promising performance that leads to head impact avoidance and mitigation of hip impact velocity. The motivation for the current research was to extend the application of previously developed optimization techniques to study more realistic human-LLE fall conditions. We examined a range of feasible fall durations for the human-LLE model and found the optimal fall duration for which the user's safety is maximized. Next, we used a range of coefficients of friction to examine fall strategies on different ground surface conditions. We found that the effectiveness of a safe-fall strategy is higher when falling on less slippery surfaces compared to more slippery ones. The simulation results were implemented in a half-scale physical model of a three-link inverted pendulum, which represented a human-LLE model. Results of our experiments verified that the optimal safe-fall strategy could be implemented in a mechanical test setup. The hip linear velocity at impact was found to have similar values in both the experimental (2.04 m/s) and simulation results (2.09 m/s). Further studies should be conducted with appropriate software and hardware platforms to successfully implement safe-fall strategies in an actual LLE.

There has been a growth in the design and use of power assist devices for manual wheelchairs (MWCs) to alleviate the physical load of MWC use. A pushrim-activated power-assisted wheel (PAPAW) is an example of a power assist device that replaces the conventional wheel of a MWC. Although the use of PAPAWs provides some benefits to MWC users, it can also cause difficulties in maneuvering the wheelchair. In this research, we examined the characteristics of wheelchair propulsion when using manual and powered wheels. We used the left and right wheels' angular velocity to calculate the linear and angular velocity of the wheelchair. Results of this analysis revealed that the powered wheel's controller is not optimally designed to reflect the intentions of a wheelchair user. To address some of the challenges with coordinating the pushes on PAPAWs, we proposed the design of a user-intention detection framework. We used the kinematic data of MWC experiments and tested six supervised learning algorithms to classify one of four movements: "not moving", "moving straight forward", "turning left", and "turning right". We found that all the classification algorithms determined the type of movement with high accuracy and low computation time. The proposed intention detection framework can be used in the design of learning-based controllers for PAPAWs that take into account the individualized characteristics of wheelchair users. Such a system may improve the experience of PAPAW users.

Identifying the appropriate needs for biomedical device design is challenging, especially for less structured environments. The paper proposes an alternate need-finding method based on Cultural Historical Activity Theory and expanded to explicitly examine the role of devices within a socioeconomic system. This is compared to a conventional need-finding technique in a preliminary study with engineering student teams. The initial results show that the Activity Theory-based technique allows teams to gain deeper insights into their needs space.

Learning to maintain standing balance in the presence of a paretic limb is an important recovery process for many stroke survivors. In this study, we used a robotic balance simulator to investigate whether manipulating medial-lateral or anterior-posterior torque contributions (i.e. input gains) could shift the control of balance toward a targeted lower limb in healthy controls. Manipulation of medial-lateral (ML) torque gains shifted the vertical load distribution toward the virtually weakened limb, but did not result in a significant shift in anterior-posterior (AP) torque control. Instead individual participants were observed to shift AP torque control in either direction, although participants more often shifted control toward the virtually weakened limb at larger ML asymmetries. In contrast, manipulation of AP torque gains did not produce any observable changes in measured torque signals. The shift in torque contributions during ML manipulations shows promise as an implicit training method for reducing weight-bearing asymmetry. However, further work is required to ensure both vertical load and AP torque control shift in the desired direction as well as to determine the applicability of the protocol in a patient population.

For over 25 years, personal assistant robots for severely disabled individuals have been in development. More recently, using robots to deliver rehabilitation therapy has been proposed. This paper summarizes the development and clinical testing of three mechatronic systems for post-stroke therapy conducted at the VA Palo Alto in collaboration with Stanford University. We describe the philosophy and experiences that guided their evolution. Unique to the Palo Alto approach is provision for bimanual, mirror-image, patient-controlled therapeutic exercise. Proof-of-concept was established with a 2-degree-of-freedom (DOF) elbow/forearm manipulator. Tests of a second-generation therapy robot producing planar forearm movements in 19 hemiplegic and control subjects confirmed the validity and reliability of interaction forces during mechanically assisted upper-limb movements. Clinical trials comparing 3-D robot-assisted therapy to traditional therapy in 21 chronic stroke subjects showed significant improvement in the Fugl-Meyer (FM) measure of motor recovery in the robot group, which exceeded improvements in the control group.