Humanoid robots as teaching assistants in an arab school

© 2019 Association for Computing Machinery. The proliferation of educational robots has led to an investigation of suitable roles that humanoids robots can take in the classroom. In the recent past, the focus has been on humanoids being used in student focused roles or as peer learners. Coupled with the seemingly absence of any case studies of educational robots in the United Arab Emirates (UAE) or the Arab world, we present a study where we employed the Nao robot as a teaching assistant in a local primary school in Abu Dhabi, UAE. The Nao robot was used to revise a topic in Mathematics and its efficacy in comparison to a human teaching assistant was measured through pre and post test scores, facial expressions and indirect verbal responses. Our results showed that while there no significant differences in test scores, the children were much more engaged when interacting with the Nao robot. We conclude with a positive outlook towards the implementation of humanoid robots in UAE classrooms

Generation of Human-Like Movement from Symbolized Information

An important function missing from current robotic systems is a human-like method for creating behavior from symbolized information. This function could be used to assess the extent to which robotic behavior is human-like because it distinguishes human motion from that of human-made machines created using currently available techniques. The purpose of this research is to clarify the mechanisms that generate automatic motor commands to achieve symbolized behavior. We design a controller with a learning method called tacit learning, which considers system–environment interactions, and a transfer method called mechanical resonance mode, which transfers the control signals into a mechanical resonance mode space (MRM-space). We conduct simulations and experiments that involve standing balance control against disturbances with a two-degree-of-freedom inverted pendulum and bipedal walking control with humanoid robots. In the simulations and experiments on standing balance control, the pendulum can become upright after a disturbance by adjusting a few signals in MRM-space with tacit learning. In the simulations and experiments on bipedal walking control, the robots realize a wide variety of walking by manually adjusting a few signals in MRM-space. The results show that transferring the signals to an appropriate control space is the key process for reducing the complexity of the signals from the environment and achieving diverse behavior

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Motor Control System for Adaptation of Healthy Individuals and Recovery of Poststroke Patients: A Case Study on Muscle Synergies

Understanding the complex neuromuscular strategies underlying behavioral adaptation in healthy individuals and motor recovery after brain damage is essential for gaining fundamental knowledge on the motor control system. Relying on the concept of muscle synergy, which indicates the number of coordinated muscles needed to accomplish specific movements, we investigated behavioral adaptation in nine healthy participants who were introduced to a familiar environment and unfamiliar environment. We then compared the resulting computed muscle synergies with those observed in 10 moderate-stroke survivors throughout an 11-week motor recovery period. Our results revealed that computed muscle synergy characteristics changed after healthy participants were introduced to the unfamiliar environment, compared with those initially observed in the familiar environment, and exhibited an increased neural response to unpredictable inputs. The altered neural activities dramatically adjusted through behavior training to suit the unfamiliar environment requirements. Interestingly, we observed similar neuromuscular behaviors in patients with moderate stroke during the follow-up period of their motor recovery. This similarity suggests that the underlying neuromuscular strategies for adapting to an unfamiliar environment are comparable to those used for the recovery of motor function after stroke. Both mechanisms can be considered as a recall of neural pathways derived from preexisting muscle synergies, already encoded by the brain's internal model. Our results provide further insight on the fundamental principles of motor control and thus can guide the future development of poststroke therapies.We are very grateful for the technical and financial assistance of Toyota Motor Co

Motor Control System for Adaptation of Healthy Individuals and Recovery of Poststroke Patients: A Case Study on Muscle Synergies

Understanding the complex neuromuscular strategies underlying behavioral adaptation in healthy individuals and motor recovery after brain damage is essential for gaining fundamental knowledge on the motor control system. Relying on the concept of muscle synergy, which indicates the number of coordinated muscles needed to accomplish specific movements, we investigated behavioral adaptation in nine healthy participants who were introduced to a familiar environment and unfamiliar environment. We then compared the resulting computed muscle synergies with those observed in 10 moderate-stroke survivors throughout an 11-week motor recovery period. Our results revealed that computed muscle synergy characteristics changed after healthy participants were introduced to the unfamiliar environment, compared with those initially observed in the familiar environment, and exhibited an increased neural response to unpredictable inputs. The altered neural activities dramatically adjusted through behavior training to suit the unfamiliar environment requirements. Interestingly, we observed similar neuromuscular behaviors in patients with moderate stroke during the follow-up period of their motor recovery. This similarity suggests that the underlying neuromuscular strategies for adapting to an unfamiliar environment are comparable to those used for the recovery of motor function after stroke. Both mechanisms can be considered as a recall of neural pathways derived from preexisting muscle synergies, already encoded by the brain’s internal model. Our results provide further insight on the fundamental principles of motor control and thus can guide the future development of poststroke therapies

Quantification of Extent of Muscle-skin Shifting by Traversal sEMG Analysis Using High-density sEMG Sensor

Surface electromyography(sEMG) measurement has been an essential approach to analyze human behaviors because we can generally consider that sEMG signals represent the muscle activities as the final output of our nerve system. One of the most serious problems for considering sEMG signal as the muscle activity is the shift of the relative position between muscles and skin depending on a posture. The motion of forearm rotation is the prominent example of muscle-skin shifting depending on postural changes. The sEMG signal from a sensor may represent the different muscle activity when the muscle-skin shifting is happened. In this study, we discuss a method to quantify the muscle-skin shift from the sEMG signals in response to the postural changes. We use the high density sEMG sensor that is possible to measure sEMG signal as the potential map. We proposed the computation algorithm to quantify the amount of muscle-skin shifting based on the change of the sEMG signals in response to the postural changes. We conducted the experiments of wrist extension motions under three different forearm postures: forearm pronation, natural posture and forearm supination. Experimental results from three healthy subjects show that we can quantify the extent of muscle-skin shifting as an angle by using proposed algorithm

Theoretical approach for designing the rehabilitation robot controller

Robot rehabilitation is now recognized as an important method for the efficient recovery. In European Project FP7 BioMot, we have discussed the potential of the robot rehabilitation and proposed the suitable process for it. In this paper, we describe the proposed rehabilitation process and create the theoretical basis for the robot rehabilitation through designing the control system and the patient model. To design the patient model, we describe the source of paralysis and motion controller separately and define the recovery function from the paralysis. In the theoretical analysis of the control system, we show that the robot motions are first adapted to the patient abnormal motions and gradually drive the patient motions to the better ones by the motion support. The singular perturbation analysis proves that the stabilities of the two different process, adaptation to the patient motions and the motion support to the better ones, as a slow motion subsystem and a fast motion subsystem, respectively. The simulation results show that the proposed control system can drive the patients to the better state depending on the patient conditions such as recovery speed and recovery potential. The proposed system can be tuned to fit to the variety of the real patient conditions when we apply it to the real applications.This study was funded by a grant from the European Commission within its Seventh Framework Programme (IFP7-ICT-2013-10-611695: BioMot – Smart Wearable Robots with Bioinspired Sensory-Motor Skills)

Haptic Adaptive Feedback to Promote Motor Learning With a Robotic Ankle Exoskeleton Integrated With a Video Game

Background: Robotic devices have been used to rehabilitate walking function after stroke. Although results suggest that post-stroke patients benefit from this non-conventional therapy, there is no agreement on the optimal robot-assisted approaches to promote neurorecovery. Here we present a new robotic therapy protocol using a grounded exoskeleton perturbing the ankle joint based on tacit learning control. Method: Ten healthy individuals and a post-stroke patient participated in the study and were enrolled in a pilot intervention protocol that involved performance of ankle movements following different trajectories via video game visual feedback. The system autonomously modulated task difficulty according to the performance to increase the challenge. We hypothesized that motor learning throughout training sessions would lead to increased corticospinal excitability of dorsi-plantarflexor muscles. Transcranial Magnetic Stimulation was used to assess the effects on corticospinal excitability. Results: Improvements have been observed on task performance and motor outcomes in both healthy individuals and post-stroke patient case study. Tibialis Anterior corticospinal excitability increased significantly after the training; however no significant changes were observed on Soleus corticospinal excitability. Clinical scales showed functional improvements in the stroke patient. Discussion and Significance: Our findings both in neurophysiological and performance assessment suggest improved motor learning. Some limitations of the study include treatment duration and intensity, as well as the non-significant changes in corticospinal excitability obtained for Soleus. Nonetheless, results suggest that this robotic training framework is a potentially interesting approach that can be explored for gait rehabilitation in post-stroke patients.This research has been funded by the Commission of the European Union under the BioMot project–Smart Wearable Robots with Bioinspired Sensory-Motor Skills (Grant Agreement number IFP7-ICT-2013-10-611695), also under the ASTONISH Project–Advancing Smart Optical Imaging and Sensing for Health (Grant Agreement number H2020-EU.2.1.1.7.-ECSEL-04-2015-692470); with financial support of Spanish Ministry of Economy and Competitiveness (MINECO) under the ASSOCIATE project—A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury (Grant Agreement number 799158449-58449-45-514); and with grant RYC-2014-16613, also by Spanish Ministry of Economy and Competitiveness

Video_1_Generation of Human-Like Movement from Symbolized Information.MP4

<p>An important function missing from current robotic systems is a human-like method for creating behavior from symbolized information. This function could be used to assess the extent to which robotic behavior is human-like because it distinguishes human motion from that of human-made machines created using currently available techniques. The purpose of this research is to clarify the mechanisms that generate automatic motor commands to achieve symbolized behavior. We design a controller with a learning method called tacit learning, which considers system–environment interactions, and a transfer method called mechanical resonance mode, which transfers the control signals into a mechanical resonance mode space (MRM-space). We conduct simulations and experiments that involve standing balance control against disturbances with a two-degree-of-freedom inverted pendulum and bipedal walking control with humanoid robots. In the simulations and experiments on standing balance control, the pendulum can become upright after a disturbance by adjusting a few signals in MRM-space with tacit learning. In the simulations and experiments on bipedal walking control, the robots realize a wide variety of walking by manually adjusting a few signals in MRM-space. The results show that transferring the signals to an appropriate control space is the key process for reducing the complexity of the signals from the environment and achieving diverse behavior.</p