Proceedings of the ... IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics最新文献

Strength and selective motor control are primary determinants of pathological gait in children with cerebral palsy (CP) and other neuromotor disorders. Emerging evidence suggests robotic application of task-specific resistance to functional movements may provide the opportunity to strengthen muscles and improve neuromuscular function during walking in children with CP. Such a strategy could be most beneficial to children who are more severely affected by the pathology but their ability to overcome such resistance and maintain functional ambulation remains unclear. The goal of this study was to design, validate and evaluate initial feasibility and effects of a novel exoskeleton strategy that provides interleaved assistance and resistance to knee extension during overground walking. One participant with CP (GMFCS III) was recruited and completed ten total visits, nine walking with the exoskeleton. Our results validated the controller's ability to parse the gait cycle into five discrete phases (mean accuracy 91%) and provide knee extension assistance during stance and resistance during swing. Following acclimation to the interleaved strategy, peak knee extension was significantly improved in both the left (mean 7.9 deg) and right (15.2 deg) limbs when walking with the exoskeleton. Knee extensor EMG during late swing phase increased to 2.7 (left leg) and 1.7 (right leg) times the activation level during baseline exoskeleton walking without resistance. These results indicate that this interleaved strategy warrants further investigation in a longitudinal intervention study, particularly in individuals who may be more severely affected such that they are unable to ambulate overground using an exoskeleton training strategy that only deploys targeted resistance to limb motion.

In this study we present a new approach to plan a high-dose-rate (HDR) prostate brachytherapy (BT) using active needles recently developed by our group. The active needles realize bi-directional bending inside the tissue, and thereby more compliant with the patient's anatomy compared with conventional straight needles. A computational method is presented to first generate a needle arrangement configuration based on the patient's prostate anatomy. The needle arrangement is generated to cover the prostate volume, providing accessible channels for the radiation source during a HDR BT. The needle arrangement configuration avoids healthy organs and prevents needle collision inside the body. Then a treatment plan is proposed to ensure sufficient prescribed dosage to the whole prostate gland. The method is applied to a prostate model reconstructed from an anonymized patient to show the feasibility of this method. Finally, the active needle's capability to generate the required bending is shown. We have shown that our method is able to automatically generate needle arrangement configuration using active needles, and plan for a treatment that meets the dose objectives while using fewer needles (about 20% of conventional straight needles) than the conventional HDR BT performed by straight needles.

Surgical movements have an important stylistic quality that individuals without formal surgical training can use to identify expertise. In our prior work, we sought to characterize quantitative metrics associated with surgical style and developed a near-real-time detection framework for stylistic deficiencies using a commercial haptic device. In this paper, we implement bimanual stylistic detection on the da Vinci Research Kit (dVRK) and focus on one stylistic deficiency, "Anxious", which may describe movements under stressful conditions. Our goal is to potentially correct these "Anxious" movements by exploring the effects of three different types of haptic cues (time-variant spring, damper, and spring-damper feedback) on performance during a basic surgical training task using the da Vinci Research Kit (dVRK). Eight subjects were recruited to complete peg transfer tasks using a randomized order of haptic cues and with baseline trials between each task. Overall, all cues lead to a significant improvement over baseline economy of volume and time-variant spring haptic cues lead to significant improvements in reducing the classified "Anxious" movements and also corresponded with significantly lower path length and economy of volume for the non-dominant hand. This work is the first step in evaluating our stylistic detection model on a surgical robot and could lay the groundwork for future methods to actively and adaptively reduce the negative effect of stress in the operating room.

Infants at risk for developmental delays often exhibit postures and movements that may provide a window into potential impairment for cerebral palsy and other neuromotor conditions. We developed a simple 4 DOF robot pediatric simulator to help provide insight into how infant kinematic movements may affect the center of pressure (COP), a common measure thought to be sensitive to neuromotor delay when assessed from supine infants at play. We conducted two experiments: 1) we compared changes in COP caused by limb movements to a human infant and 2) we determined if we could predict COP position due to limb movements using simulator kinematic pose retrieved from video and a sensorized mat. Our results indicate that the limb movements alone were not sufficient to mimic the COP in a human infant. In addition, we show that given a robot simulator and a simple camera, we can predict COP measured by a force sensing mat. Future directions suggest a more complex robot is needed such as one that may include trunk DOF.

Research, development and testing of wearable robotic exoskeletons for gait training and improved mobility in children with cerebral palsy (CP) and other movement disorders has become increasingly prevalent in recent years. Broadly, these devices are split into two categories: fully wearable devices that are affixed to the lower limbs and/or pelvis and systems that include a mobile frame that moves with the user. The former systems have generally targeted more functional individuals who are independent walkers while the latter target more affected individuals who do not ambulate on their own. The best strategy for children in the middle of this mobility spectrum (GMFCS III), who can walk short distances using assistive instruments like crutches or walkers, is not clear. Yet, these children may benefit most from gait training because they are at the highest risk to lose their independent mobility. Here, we present a case study of a wearable robotic exoskeleton for overground walking in a child with CP at GMFCS III. Our results demonstrate that the exoskeleton was able to synchronize assistance to five discrete phases of the gait cycle during overground walking with forearm crutches. Peak knee extension improved on average by 10 degrees in the right leg and 7 degrees in the left leg when walking with exoskeleton assistance during early stance, mid-stance and late swing without reduction in muscle activity. Therefore, state-based control for providing robotic extension assistance to individuals with crouch from CP who walk with assistive instruments should be further investigated as a potential rehabilitation strategy.

Acquisition of surface electromyography (sEMG) from a person with an amputated lower extremity (LE) during prosthesis-assisted walking remains a significant challenge due to the dynamic nature of the gait cycle. Current solutions to sEMG-based neural control of active LE prostheses involve a combination of customized electrodes, prosthetic sockets, and liners. These technologies are generally: (i) incompatible with a subject's existing prosthetic socket and liners; (ii) uncomfortable to use; and (iii) expensive. This paper presents a flexible dry electrode design for sEMG acquisition within LE prosthetic sockets which seeks to address these issues. Design criteria and corresponding design decisions are explained and a proposed flexible electrode prototype is presented. Performances of the proposed electrode and commercial Ag/AgCl electrodes are compared in seated subjects without amputations. Quantitative analyses suggest comparable signal qualities for the proposed novel electrode and commercial electrodes. The proposed electrode is demonstrated in a subject with a unilateral transtibial amputation wearing her own liner, socket, and the portable sEMG processing platform in a preliminary standing and level ground walking study. Qualitative analyses suggest the feasibility of real-time sEMG data collection from load-bearing, ambulatory subjects.

The World Health Organization estimates that 15 million infants are born preterm every year [1]. This is of concern because these infants have a significant chance of having neuromotor or cognitive developmental delays due to cerebral palsy or other developmental issues [2]. Our long-term goal is to determine the roles emotion and movement play in the diagnosis of atypical infants. In this paper, we examine how automated emotion assessment may have potential to classify typically and atypically developing infants. We compare a custom supervised machine learning algorithm that utilizes individual and grouped facial features for infant emotion classification with a state-of-the-art neural network. Our results show that only three concavity features are needed for the concavity algorithm, and the custom algorithm performed with relatively similar performance to the neural network. Automatic sentiment labels used in tandem with infant movement kinematics would be further investigated to determine if emotion and movement are interdependent and predictive of an infant's neurodevelopmental delay in disorders such as cerebral palsy.

Stroke often results in chronic motor impairment of the upper-extremity yet neither traditional- nor robotics-based therapy has been able to affect this in a profound way. Supporting the weak affected shoulder against gravity improves reaching distance and minimizes abnormal co-contraction of the elbow, wrist, and fingers after stroke. However, it is necessary to assess the feasibility and efficacy of real-time controllers for this population as technology advances and a wearable shoulder device comes closer to reality. The aim of this study is to test two EMG-based controllers in this regard. A linear discriminant analysis based classifier was trained using extracted time domain and auto-regressive features from electromyographic data acquired during muscle effort required to move a load equivalent to 50 and 100% limb weight (abduction) and 150 and 200% limb weight (adduction). While rigidly connected to a custom lab-based robot, the participant was required to complete a series of lift and reach tasks under two different control paradigms: position-based control and force-based control. The participant successfully controlled the robot under both paradigms as indicated by first moving the robot arm into the proper vertical window and then reaching out as far as possible while remaining within the vertical window. This case study begins to assess the feasibility of using electromyographic data to classify the intended shoulder movement of a participant with stroke during a functional lift and reach type task. Next steps will assess how this type of support affects reaching function.

Individuality in clinical gait analysis is often quantified by an individual's kinematic deviation from the norm, but it is unclear how these deviations generalize across different walking speeds and ground slopes. Understanding individuality across tasks has important implications in the tuning of prosthetic legs, where clinicians have limited time and resources to personalize the kinematic motion of the leg to therapeutically enhance the wearer's gait. This study seeks to determine an efficient way to predictively model an individual's kinematics over a continuous range of slopes and speeds given only one personalized task at level ground. We were able to predict the kinematics of able-bodied individuals at a wide variety of conditions that were not specifically tuned. Applied to 10 human subjects, the individualization method reduced the RMSE between the model and subject's kinematics over all tasks by an average of 2% (max 52%) at the ankle, 27% (max 59%) at the knee, and 45% (max 83%) at the hip. Our results indicate that knowing how an individual subject differs from the average subject at level ground alone is enough information to improve kinematic predictions across all tasks. This research offers a new method for personalizing robotic prosthetic legs over a variety of tasks without the need of an engineer, which could make these complex devices more clinically viable.