An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search

Robots have been used to model nature, while nature in turn can contribute to the real-world artifacts we construct. One particular domain of interest is chemical search where a number of efforts are underway to construct mobile chemical search and localization systems. We report on a project that aims at constructing such a system based on our understanding of the pheromone communication system of the moth. Based on an overview of the peripheral processing of chemical cues by the moth and its role in the organization of behavior we emphasize the multimodal aspects of chemical search, i.e. optomotor anemotactic chemical search. We present a model of this behavior that we test in combination with a novel thin metal oxide sensor and custom build mobile robots. We show that the sensor is able to detect the odor cue, ethanol, under varying flow conditions. Subsequently we show that the standard model of insect chemical search, consisting of a surge and cast phases, provides for robust search and localization performance. The same holds when it is augmented with an optomotor collision avoidance model based on the Lobula Giant Movement Detector (LGMD) neuron of the locust. We compare our results to others who have used the moth as inspiration for the construction of odor robot

Interactive visuo-motor therapy system for stroke rehabilitation

We present a virtual reality (VR)-based motor neurorehabilitation system for stroke patients with upper limb paresis. It is based on two hypotheses: (1) observed actions correlated with self-generated or intended actions engage cortical motor observation, planning and execution areas ("mirror neurons”); (2) activation in damaged parts of motor cortex can be enhanced by viewing mirrored movements of non-paretic limbs. We postulate that our approach, applied during the acute post-stroke phase, facilitates motor re-learning and improves functional recovery. The patient controls a first-person view of virtual arms in tasks varying from simple (hitting objects) to complex (grasping and moving objects). The therapist adjusts weighting factors in the non-paretic limb to move the paretic virtual limb, thereby stimulating the mirror neuron system and optimizing patient motivation through graded task success. We present the system's neuroscientific background, technical details and preliminary result

Interactive visuo-motor therapy system for stroke rehabilitation

We present a virtual reality (VR)-based motor neurorehabilitation system for stroke patients with upper limb paresis. It is based on two hypotheses: (1) observed actions correlated with self-generated or intended actions engage cortical motor observation, planning and execution areas ("mirror neurons"); (2) activation in damaged parts of motor cortex can be enhanced by viewing mirrored movements of non-paretic limbs. We postulate that our approach, applied during the acute post-stroke phase, facilitates motor re-learning and improves functional recovery. The patient controls a first-person view of virtual arms in tasks varying from simple (hitting objects) to complex (grasping and moving objects). The therapist adjusts weighting factors in the non-paretic limb to move the paretic virtual limb, thereby stimulating the mirror neuron system and optimizing patient motivation through graded task success. We present the system's neuroscientific background, technical details and preliminary results.info:eu-repo/semantics/publishedVersio

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A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization, altitude control and collision avoidanc

Home-based virtual reality-augmented training improves lower limb muscle strength, balance, and functional mobility following chronic incomplete spinal cord injury

Key factors positively influencing rehabilitation and functional recovery after spinal cord injury (SCI) include training variety, intensive movement repetition, and motivating training tasks. Systems supporting these aspects may provide profound gains in rehabilitation, independent of the subject's treatment location. In the present study, we test the hypotheses that virtual reality (VR)-augmented training at home (i.e., unsupervised) is feasible with subjects with an incomplete SCI (iSCI) and that it improves motor functions such as lower limb muscle strength, balance, and functional mobility. In the study, 12 chronic iSCI subjects used a home-based, mobile version of a lower limb VR training system. The system included motivating training scenarios and combined action observation and execution. Virtual representations of the legs and feet were controlled via movement sensors. The subjects performed home-based training over 4 weeks, with 16-20 sessions of 30-45 min each. The outcome measures assessed were the Lower Extremity Motor Score (LEMS), Berg Balance Scale (BBS), Timed Up and Go (TUG), Spinal Cord Independence Measure mobility, Walking Index for Spinal Cord Injury II, and 10 m and 6 min walking tests. Two pre-treatment assessment time points were chosen for outcome stability: 4 weeks before treatment and immediately before treatment. At post-assessment (i.e., immediately after treatment), high motivation and positive changes were reported by the subjects (adapted Patients' Global Impression of Change). Significant improvements were shown in lower limb muscle strength (LEMS, P = 0.008), balance (BBS, P = 0.008), and functional mobility (TUG, P = 0.007). At follow-up assessment (i.e., 2-3 months after treatment), functional mobility (TUG) remained significantly improved (P = 0.005) in contrast to the other outcome measures. In summary, unsupervised exercises at home with the VR training system led to beneficial functional training effects in subjects with chronic iSCI, suggesting that it may be useful as a neurorehabilitation tool. Trial registration: Canton of Zurich ethics committee (EK-24/2009, PB_2016-00545), ClinicalTrials.gov: NCT02149186. Registered 24 April 2014

Intensive virtual reality-based training for upper limb motor function in chronic stroke: a feasibility study using a single case experimental design and fMRI

Purpose: To evaluate feasibility and neurophysiological changes after virtual reality (VR)-based training of upper limb (UL) movements. Method: Single-case A-B-A-design with two male stroke patients (P1:67 y and 50 y, 3.5 and 3 y after onset) with UL motor impairments, 45-min therapy sessions 5×/week over 4 weeks. Patients facing screen, used bimanual data gloves to control virtual arms. Three applications trained bimanual reaching, grasping, hand opening. Assessments during 2-week baseline, weekly during intervention, at 3-month follow-up (FU): Goal Attainment Scale (GAS), Chedoke Arm and Hand Activity Inventory (CAHAI), Chedoke-McMaster Stroke Assessment (CMSA), Extended Barthel Index (EBI), Motor Activity Log (MAL). Functional magnetic resonance imaging scans (FMRI) before, immediately after treatment and at FU. Results: P1 executed 5478 grasps (paretic arm). Improvements in CAHAI (+4) were maintained at FU. GAS changed to +1 post-test and +2 at FU. P2 executed 9835 grasps (paretic arm). CAHAI improvements (+13) were maintained at FU. GAS scores changed to -1 post-test and +1 at FU. MAL scores changed from 3.7 at pre-test to 5.5 post-test and 3.3 at FU. Conclusion: The VR-based intervention was feasible, safe, and intense. Adjustable application settings maintained training challenge and patient motivation. ADL-relevant UL functional improvements persisted at FU and were related to changed cortical activation patterns. Implications for Rehabilitation YouGrabber trains uni- and bimanual upper motor function. Its application is feasible, safe, and intense. The control of the virtual arms can be done in three main ways: (a) normal (b) virtual mirror therapy, or (c) virtual following. The mirroring feature provides an illusion of affected limb movements during the period when the affected upper limb (UL) is resting. The YouGrabber training led to ADL-relevant UL functional improvements that were still assessable 12 weeks after intervention finalization and were related to changed cortical activation patterns

Virtual reality-augmented neurorehabilitation improves motor function and reduces neuropathic pain in patients with incomplete spinal cord injury

BACKGROUND: Neurorehabilitation interventions to improve lower limb function and neuropathic pain have had limited success in people with chronic, incomplete spinal cord injury (iSCI). OBJECTIVE: We hypothesized that intense virtual reality (VR)-augmented training of observed and executed leg movements would improve limb function and neuropathic pain. METHODS: . Patients used a VR system with a first-person view of virtual lower limbs, controlled via movement sensors fitted to the patient's own shoes. Four tasks were used to deliver intensive training of individual muscles (tibialis anterior, quadriceps, leg ad-/abductors). The tasks engaged motivation through feedback of task success. Fourteen chronic iSCI patients were treated over 4 weeks in 16 to 20 sessions of 45 minutes. Outcome measures were 10 Meter Walking Test, Berg Balance Scale, Lower Extremity Motor Score, Spinal Cord Independence Measure, Locomotion and Neuropathic Pain Scale (NPS), obtained at the start and at 4 to 6 weeks before intervention. RESULTS: In addition to positive changes reported by the patients (Patients' Global Impression of Change), measures of walking capacity, balance, and strength revealed improvements in lower limb function. Intensity and unpleasantness of neuropathic pain in half of the affected participants were reduced on the NPS test. Overall findings remained stable 12 to 16 weeks after termination of the training. CONCLUSIONS: In a pretest/posttest, uncontrolled design, VR-augmented training was associated with improvements in motor function and neuropathic pain in persons with chronic iSCI, several of which reached the level of a minimal clinically important change. A controlled trial is needed to compare this intervention to active training alone or in combination

A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization, altitude control and collision avoidance

The most versatile and robust flying machines are still those produced by nature through evolution. The solutions to the 6 DOF control problem faced by these machines are implemented in extremely small neuronal structures comprising thousands of neurons. Hence, the biological principles of flight control are not only very effective but also efficient in terms of their implementation. An important question is to what extent these principles can be generalized to man-made flying platforms. Here, this question is investigated in relation to the computational and behavioral principles of the opto-motor system of the fly and locust. The aim is to provide a control infrastructure based only on biologically plausible and realistic neuronal models of the insect opto-motor system. It is shown that relying solely on vision, biologically constrained neuronal models of the fly visual system suffice for course stabilization and altitude control of a blimp-based UAV. Moreover, the system is augmented with a collision avoidance model based on the Lobula Giant Movement Detector neuron of the Locust. It is shown that the biologically constrained course stabilization model is highly robust and that the combined model is able to perform autonomous indoor flight