Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.

Underwater video monitoring systems are being widely used in fisheries to investigate fish behavior in relation to fishing gear and fishing gear performance during fishing. Such systems can be useful to evaluate the catch composition as well. In demersal trawl fisheries, however, their applicability can be challenged by low light conditions, mobilized sediment and scattering in murky waters. In this study, we introduce a novel observation system (called NepCon) which aims at reducing current limitations by combining an optimized image acquisition setup and tailored image analyses software. The NepCon system includes a high-contrast background to enhance the visibility of the target objects, a compact camera and an artificial light source. The image analysis software includes a machine learning algorithm which is evaluated here to test automatic detection and count of Norway lobster (Nephrops norvegicus). NepCon is specifically designed for applications in demersal trawls and this first phase aims at increasing the accuracy of N. norvegicus detection at the data acquisition level. To find the best contrasting background for the purpose we compared the output of four image segmentation methods applied to static images of N. norvegicus fixed in front of four test background colors. The background color with the best performance was then used to evaluate computer vision and deep learning approaches for automatic detection, tracking and counting of N. norvegicus in the videos. In this initial phase we tested the system in an experimental setting to understand the feasibility of the system for future implementation in real demersal fishing conditions. The N. norvegicus directed trawl fishery typically has no assistance from underwater observation technology and therefore are largely conducted blindly. The demonstrated perception system achieves 76% accuracy (F-score) in automatic detection and count of N. norvegicus, which provides a significant elevation of the current benchmark.

A resonant squid-inspired robot unlocks biological propulsive efficiency.

Elasticity has been linked to the remarkable propulsive efficiency of pulse-jet animals such as the squid and jellyfish, but reports that quantify the underlying dynamics or demonstrate its application in robotic systems are rare. This work identifies the pulse-jet propulsion mode used by these animals as a coupled mass-spring-mass oscillator, enabling the design of a flexible self-propelled robot. We use this system to experimentally demonstrate that resonance greatly benefits pulse-jet swimming speed and efficiency, and the robot's optimal cost of transport is found to match that of the most efficient biological swimmers in nature, such as the jellyfish Aurelia aurita The robot also exhibits a preferred Strouhal number for efficient swimming, thereby bridging the gap between pulse-jet propulsion and established findings in efficient fish swimming. Extensions of the current robotic framework to larger amplitude oscillations could combine resonance effects with optimal vortex formation to further increase propulsive performance and potentially outperform biological swimmers altogether.

Ischemic stroke-induced polyaxonal innervation at the neuromuscular junction is attenuated by robot-assisted mechanical therapy.

Ischemic stroke is a leading cause of disability world-wide. Mounting evidence supports neuromuscular pathology following stroke, yet mechanisms of dysfunction and therapeutic action remain undefined. The objectives of our study were to investigate neuromuscular pathophysiology following ischemic stroke and to evaluate the therapeutic effect of Robot-Assisted Mechanical massage Therapy (RAMT) on neuromuscular junction (NMJ) morphology. Using an ischemic stroke model in male rats, we demonstrated longitudinal losses of muscle contractility and electrophysiological estimates of motor unit number in paretic hindlimb muscles within 21 days of stroke. Histological characterization demonstrated striking pre- and postsynaptic alterations at the NMJ. Stroke prompted enlargement of motor axon terminals, acetylcholine receptor (AChR) area, and motor endplate size. Paretic muscle AChRs were also more homogenously distributed across motor endplates, exhibiting fewer clusters and less fragmentation. Most interestingly, NMJs in paretic muscle exhibited increased frequency of polyaxonal innervation. This finding of increased polyaxonal innervation in stroke-affected skeletal muscle suggests that reduction of motor unit number following stroke may be a spurious artifact due to overlapping of motor units rather than losses. Furthermore, we tested the effects of RAMT - which we recently showed to improve motor function and protect against subacute myokine disturbance - and found significant attenuation of stroke-induced NMJ alterations. RAMT not only normalized the post-stroke presentation of polyaxonal innervation but also mitigated postsynaptic expansion. These findings confirm complex neuromuscular pathophysiology after stroke, provide mechanistic direction for ongoing research, and inform development of future therapeutic strategies. SIGNIFICANCE: Ischemic stroke is a leading contributor to chronic disability, and there is growing evidence that neuromuscular pathology may contribute to the impact of stroke on physical function. Following ischemic stroke in a rat model, there are progressive declines of motor unit number estimates and muscle contractility. These changes are paralleled by striking pre- and postsynaptic maladaptive changes at the neuromuscular junction, including polyaxonal innervation. When administered to paretic hindlimb muscle, Robot-Assisted Mechanical massage Therapy - previously shown to improve motor function and protect against subacute myokine disturbance - prevents stroke-induced neuromuscular junction alterations. These novel observations provide insight into the neuromuscular response to cerebral ischemia, identify peripheral mechanisms of functional disability, and present a therapeutic rehabilitation strategy with clinical relevance.

Mobile robotic platforms for the acoustic tracking of deep-sea demersal fishery resources.

Knowing the displacement capacity and mobility patterns of industrially exploited (i.e., fished) marine resources is key to establishing effective conservation management strategies in human-impacted marine ecosystems. Acquiring accurate behavioral information of deep-sea fished ecosystems is necessary to establish the sizes of marine protected areas within the framework of large international societal programs (e.g., European Community H2020, as part of the Blue Growth economic strategy). However, such information is currently scarce, and high-frequency and prolonged data collection is rarely available. Here, we report the implementation of autonomous underwater vehicles and remotely operated vehicles as an aid for acoustic long-baseline localization systems for autonomous tracking of Norway lobster (Nephrops norvegicus), one of the key living resources exploited in European waters. In combination with seafloor moored acoustic receivers, we detected and tracked the movements of 33 tagged lobsters at 400-m depth for more than 3 months. We also identified the best procedures to localize both the acoustic receivers and the tagged lobsters, based on algorithms designed for off-the-shelf acoustic tags identification. Autonomous mobile platforms that deliver data on animal behavior beyond traditional fixed platform capabilities represent an advance for prolonged, in situ monitoring of deep-sea benthic animal behavior at meter spatial scales.

Electromechanical-assisted training for walking after stroke.

BACKGROUND: Electromechanical- and robot-assisted gait-training devices are used in rehabilitation and might help to improve walking after stroke. This is an update of a Cochrane Review first published in 2007 and previously updated in 2017. OBJECTIVES: Primary • To determine whether electromechanical- and robot-assisted gait training versus normal care improves walking after stroke Secondary • To determine whether electromechanical- and robot-assisted gait training versus normal care after stroke improves walking velocity, walking capacity, acceptability, and death from all causes until the end of the intervention phase SEARCH METHODS: We searched the Cochrane Stroke Group Trials Register (last searched 6 January 2020); the Cochrane Central Register of Controlled Trials (CENTRAL; 2020 Issue 1), in the Cochrane Library; MEDLINE in Ovid (1950 to 6 January 2020); Embase (1980 to 6 January 2020); the Cumulative Index to Nursing and Allied Health Literature (CINAHL; 1982 to 20 November 2019); the Allied and Complementary Medicine Database (AMED; 1985 to 6 January 2020); Web of Science (1899 to 7 January 2020); SPORTDiscus (1949 to 6 January 2020); the Physiotherapy Evidence Database (PEDro; searched 7 January 2020); and the engineering databases COMPENDEX (1972 to 16 January 2020) and Inspec (1969 to 6 January 2020). We handsearched relevant conference proceedings, searched trials and research registers, checked reference lists, and contacted trial authors in an effort to identify further published, unpublished, and ongoing trials. SELECTION CRITERIA: We included all randomised controlled trials and randomised controlled cross-over trials in people over the age of 18 years diagnosed with stroke of any severity, at any stage, in any setting, evaluating electromechanical- and robot-assisted gait training versus normal care. DATA COLLECTION AND ANALYSIS: Two review authors independently selected trials for inclusion, assessed methodological quality and risk of bias, and extracted data. We assessed the quality of evidence using the GRADE approach. The primary outcome was the proportion of participants walking independently at follow-up. MAIN RESULTS: We included in this review update 62 trials involving 2440 participants. Electromechanical-assisted gait training in combination with physiotherapy increased the odds of participants becoming independent in walking (odds ratio (random effects) 2.01, 95% confidence interval (CI) 1.51 to 2.69; 38 studies, 1567 participants; P < 0.00001; I² = 0%; high-quality evidence) and increased mean walking velocity (mean difference (MD) 0.06 m/s, 95% CI 0.02 to 0.10; 42 studies, 1600 participants; P = 0.004; I² = 60%; low-quality evidence) but did not improve mean walking capacity (MD 10.9 metres walked in 6 minutes, 95% CI -5.7 to 27.4; 24 studies, 983 participants; P = 0.2; I² = 42%; moderate-quality evidence). Electromechanical-assisted gait training did not increase the risk of loss to the study during intervention nor the risk of death from all causes. Results must be interpreted with caution because (1) some trials investigated people who were independent in walking at the start of the study, (2) we found variation between trials with respect to devices used and duration and frequency of treatment, and (3) some trials included devices with functional electrical stimulation. Post hoc analysis showed that people who are non-ambulatory at the start of the intervention may benefit but ambulatory people may not benefit from this type of training. Post hoc analysis showed no differences between the types of devices used in studies regarding ability to walk but revealed differences between devices in terms of walking velocity and capacity. AUTHORS' CONCLUSIONS: People who receive electromechanical-assisted gait training in combination with physiotherapy after stroke are more likely to achieve independent walking than people who receive gait training without these devices. We concluded that eight patients need to be treated to prevent one dependency in walking. Specifically, people in the first three months after stroke and those who are not able to walk seem to benefit most from this type of intervention. The role of the type of device is still not clear. Further research should consist of large definitive pragmatic phase 3 trials undertaken to address specific questions about the most effective frequency and duration of electromechanical-assisted gait training, as well as how long any benefit may last. Future trials should consider time post stroke in their trial design.

Impact of smart force feedback rehabilitation robot training on upper limb motor function in the subacute stage of stroke.

OBJECTIVE: To explore the impact of rehabilitation robot training (RRT) on upper limb motor function and daily activity ability in patients with stroke. METHODS: Forty patients meeting the inclusion criteria were randomly divided into the treatment group (TRE) and the control group (CON). Group TRE was trained with an upper limb rehabilitation robot and group CON was trained with traditional occupational therapy. The training time was six weeks, and the upper limb function and daily activities were then assessed. RESULTS: (1) There was no statistical significance in the Fugl-Meyer (FM) score, Wolf Motor Function Test (WMFT) score, and Modified Barthel Index (MBI) score between the two groups before treatment (P > 0.05). (2) After treatment, the FM score, WMFT score, and MBI score were significantly higher than before treatment (P < 0.01). (3) There was no significant significance between the two groups after treatment (P > 0.05). CONCLUSIONS: Both RRT and traditional occupational therapy training are useful for the recovery of upper limb motor function and daily life ability in the sub-acute stage of stroke.

Intra-cortical brain-machine interfaces for controlling upper-limb powered muscle and robotic systems in spinal cord injury.

OBJECTIVE: Intracortical brain-machine interface (iBMI) is an assistive strategy to restore lost sensorimotor function by bridging the disrupted neural pathways to reanimate paralyzed limbs. However, to date, none of the studies explored the trade-offs between the performance criteria of different iBMI systems that decode discrete upper limb movements from intracortical neural recordings. METHODS: A systematic review of electronic databases using different MeSH terms from January 1990 to December 2019 was conducted. IBM® SPSS statistics version 25 (Released 2017, Armonk, NY: IBM) was used to evaluate for differences between groups using independent sample t-tests. RESULTS: A total of 18 patients from 15 studies were included in our analysis. The included studies involved iBMI controlled 5-robotic and 10-neuromuscular stimulated orthotics to perform skillful and coordinated movements that resulted in a clinically significant gain in tests of upper-limb functions. Pooled analysis revealed that the mean response time to execute 3-D reach and grasp task by the robotic-assisted limb was relatively longer (46.8 +/-101.5 s) compared to the neuro-muscular stimulated orthotics (15.8 +/-15.2 s); however, statistically insignificant [Mean difference (MD): 30.9, 95 % Confidence Interval (CI): -40.4-102.3, p = 0.35]. Furthermore, the accuracy in performing 3-D reach and grasp tasks after repetitive trials were better among patients with neuro-muscular stimulated orthotics (83.5 +/-12.7 %) compared to those with robotic-assisted prosthetic limb (69.1 +/- 23.6 %) with statistically significant difference (MD: 15.9, 95 % CI: 1.65-32.5, p = 0.05). CONCLUSION: Our study demonstrates that iBMI-assisted prosthetic limbs showed better accuracy and shorter response time among patients with neuro-muscular stimulated orthotics compared to robotic neuro-prosthetics.

Rise of the Robots: Is Artificial Intelligence a Friend or Foe to Nursing Practice?

Much like other aspects of health care, nursing has become increasingly saturated with technology over the past several decades. Existing technology has advanced nursing in many ways and contributed to patient safety but at the cost of decreasing nurse-patient interaction. As health care technology progresses to the inclusion of artificial intelligence (AI), the future impact on nursing and direct patient care remains largely unknown, unexplored, and difficult to predict. This article aims to explore the relevance of nursing in a technologically advanced postmodern health care system. The relevance of nursing in the future is solidified by the unique nature of nursing that includes the embodiment of human caring and emotional intelligence. Nurses' abilities to intervene before patient deterioration, care for patients holistically, and manage various aspects of care will be heightened by the adoption of AI. Nurses should embrace AI technology, as we predict that it will decrease nurse workload and cognitive overload and allow for increased patient-nurse interaction. Current and future nurses should take the lead on determining how it augments nursing practice.

A review of methods for achieving upper limb movement following spinal cord injury through hybrid muscle stimulation and robotic assistance.

Individuals with tetraplegia, typically attributed to spinal cord injuries (SCI) at the cervical level, experience significant health care costs and loss of independence due to their limited reaching and grasping capabilities. Neuromuscular electrical stimulation (NMES) is a promising intervention to restore arm and hand function because it activates a person's own paralyzed muscles; however, NMES sometimes lacks the accuracy and repeatability necessary to position the limb for functional tasks, and repeated muscle stimulation can lead to fatigue. Robotic devices have the potential to restore function when used as assistive devices to supplement or replace limited or lost function of the upper limb following SCI. Unfortunately, most robotic solutions are bulky or require significant power to operate, limiting their applicability to restore functional independence in a home environment. Combining NMES and robotic support systems into a single hybrid neuroprosthesis is compelling, since the robotic device can supplement the action of the muscles and improve repeatability and accuracy. Research groups have begun to explore applications of movement assistance for individuals with spinal cord injury using these technologies in concert. In this review, we present the state of the art in hybrid NMES-orthotic systems for upper limb movement restoration following spinal cord injury, and suggest areas for emphasis necessary to move the field forward. Currently, NMES-robotic systems use either surface or implanted electrodes to stimulate muscles, with rigid robotic supports holding the limb against gravity, or providing assistance in reaching movements. Usability of such systems outside of the lab or clinic is limited due to the complexity of both the mechanical components, stimulation systems, and human-machine interfaces. Assessment of system and participant performance is not reported in a standardized way. Future directions should address wearability through improvements in component technologies and user interfaces. Further, increased integration of the control action between NMES and robotic subsystems to reanimate the limb should be pursued. Standardized reporting of system performance and expanded clinical assessments of these systems are also needed. All of these advancements are critical to facilitate translation from lab to home.

CFD-based multi-objective controller optimization for soft robotic fish with muscle-like actuation.

Soft robots take advantage of rich nonlinear dynamics and large degrees of freedom to perform actions often by novel means beyond the capability of conventional rigid robots. Nevertheless, there are considerable challenges in analysis, design, and optimization of soft robots due to their complex behaviors. This is especially true for soft robotic swimmers whose dynamics are determined by highly nonlinear fluid-structure interactions. We present a holistic computational framework that employs a multi-objective evolutionary method to optimize feedback controllers for maneuvers of a soft robotic fish under artificial muscle actuation. The resultant fluid-structure interactions are fully solved by using a novel fictitious domain/active strain method. In particular, we consider a two-dimensional elastic plate with finite thickness, subjected to active contractile strains on both sides of the body. Compared to the conventional approaches that require specifying the entire-body curvature variation, we demonstrate that imposing contractile active strains locally can produce various swimming gaits, such as forwarding swimming and turning, using far fewer control parameters. The parameters of a pair of proportional-integral-derivative (PID) controllers, used to control the amplitude and the bias of the active strains, respectively, are optimized for tracking a moving target involving different trajectories and Reynolds numbers, with three objectives, tracking error, cost of transport, and elastic strain energy. The resulting Pareto fronts of the multi-objective optimization problem reveal the correlation and trade-off among the objectives and offer key insight into the design and control of soft swimmers.

Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback.

BACKGROUND: Add-on robot-mediated therapy has proven to be more effective than conventional therapy alone in post-stroke gait rehabilitation. Such robot-mediated interventions routinely use also visual biofeedback tools. A better understanding of biofeedback content effects when used for robotic locomotor training may improve the rehabilitation process and outcomes. METHODS: This randomized cross-over pilot trial aimed to address the possible impact of different biofeedback contents on patients' performance and experience during Lokomat training, by comparing a novel biofeedback based on online biological electromyographic information (EMGb) versus the commercial joint torque biofeedback (Rb) in sub-acute non ambulatory patients. 12 patients were randomized into two treatment groups, A and B, based on two different biofeedback training. For both groups, study protocol consisted of 12 Lokomat sessions, 6 for each biofeedback condition, 40 min each, 3 sessions per week of frequency. All patients performed Lokomat trainings as an add-on therapy to the conventional one that was the same for both groups and consisted of 40 min per day, 5 days per week. The primary outcome was the Modified Ashworth Spasticity Scale, and secondary outcomes included clinical, neurological, mechanical, and personal experience variables collected before and after each biofeedback training. RESULTS: Lokomat training significantly improved gait/daily living activity independence and trunk control, nevertheless, different effects due to biofeedback content were remarked. EMGb was more effective to reduce spasticity and improve muscle force at the ankle, knee and hip joints. Robot data suggest that Rb induces more adaptation to robotic movements than EMGb. Furthermore, Rb was perceived less demanding than EMGb, even though patient motivation was higher for EMGb. Robot was perceived to be effective, easy to use, reliable and safe: acceptability was rated as very high by all patients. CONCLUSIONS: Specific effects can be related to biofeedback content: when muscular-based information is used, a more direct effect on lower limb spasticity and muscle activity is evidenced. In a similar manner, when biofeedback treatment is based on joint torque data, a higher patient compliance effect in terms of force exerted is achieved. Subjects who underwent EMGb seemed to be more motivated than those treated with Rb.

Multi-functional soft-bodied jellyfish-like swimming.

The functionalities of the untethered miniature swimming robots significantly decrease as the robot size becomes smaller, due to limitations of feasible miniaturized on-board components. Here we propose an untethered jellyfish-inspired soft millirobot that could realize multiple functionalities in moderate Reynolds number by producing diverse controlled fluidic flows around its body using its magnetic composite elastomer lappets, which are actuated by an external oscillating magnetic field. We particularly investigate the interaction between the robot's soft body and incurred fluidic flows due to the robot's body motion, and utilize such physical interaction to achieve different predation-inspired object manipulation tasks. The proposed lappet kinematics can inspire other existing jellyfish-like robots to achieve similar functionalities at the same length and time scale. Moreover, the robotic platform could be used to study the impacts of the morphology and kinematics changing in ephyra jellyfish.

Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke.

Upper limb motor deficits in severe stroke survivors often remain unresolved over extended time periods. Novel neurotechnologies have the potential to significantly support upper limb motor restoration in severely impaired stroke individuals. Here, we review recent controlled clinical studies and reviews focusing on the mechanisms of action and effectiveness of single and combined technology-aided interventions for upper limb motor rehabilitation after stroke, including robotics, muscular electrical stimulation, brain stimulation and brain computer/machine interfaces. We aim at identifying possible guidance for the optimal use of these new technologies to enhance upper limb motor recovery especially in severe chronic stroke patients. We found that the current literature does not provide enough evidence to support strict guidelines, because of the variability of the procedures for each intervention and of the heterogeneity of the stroke population. The present results confirm that neurotechnology-aided upper limb rehabilitation is promising for severe chronic stroke patients, but the combination of interventions often lacks understanding of single intervention mechanisms of action, which may not reflect the summation of single intervention's effectiveness. Stroke rehabilitation is a long and complex process, and one single intervention administrated in a short time interval cannot have a large impact for motor recovery, especially in severely impaired patients. To design personalized interventions combining or proposing different interventions in sequence, it is necessary to have an excellent understanding of the mechanisms determining the effectiveness of a single treatment in this heterogeneous population of stroke patients. We encourage the identification of objective biomarkers for stroke recovery for patients' stratification and to tailor treatments. Furthermore, the advantage of longitudinal personalized trial designs compared to classical double-blind placebo-controlled clinical trials as the basis for precise personalized stroke rehabilitation medicine is discussed. Finally, we also promote the necessary conceptual change from 'one-suits-all' treatments within in-patient clinical rehabilitation set-ups towards personalized home-based treatment strategies, by adopting novel technologies merging rehabilitation and motor assistance, including implantable ones.

Effect of EMG-biofeedback robotic-assisted body weight supported treadmill training on walking ability and cardiopulmonary function on people with subacute spinal cord injuries - a randomized controlled trial.

BACKGROUND: Body weight supported treadmill training (BWSTT) is a frequently used approach for restoring the ability to walk after spinal cord injury (SCI). However, the duration of BWSTT is usually limited by fatigue of the therapists and patients. Robotic-assisted body weight supported treadmill training (RABWSTT) was developed to tackle the aforesaid limitation. Currently, limited randomized controlled trials are available to investigate its effectiveness, especially on cardiopulmonary function. The aim of this two-arm, parallel-group randomized controlled trial is to examine the feasibility of adapting an EMG-biofeedback system for assist-as-needed RABWSTT and its effects on walking and cardiopulmonary function in people with SCI. METHODS: Sixteen incomplete SCI subjects were recruited and randomly allocated into an intervention group or control group. The intervention group received 30 min of RABWSTT with EMG biofeedback system over the vastus lateralis muscle to enhance active participation. Dose equivalent passive lower limbs mobilization exercise was provided to subjects in the control group. RESULTS: Significant time-group interaction was found in the Walking Index for Spinal Cord Injury version II (WISCI II) (p = 0.020), Spinal Cord Independence Measure version III (SCIM III) mobility sub-score (p < 0.001), bilateral symmetry (p = 0.048), maximal oxygen consumption (p = 0.014) and peak expiratory flow rate (p = 0.048). Wilcoxon signed-rank test showed that the intervention group had significant improvement in the above-mentioned outcomes after the intervention except WISCI II, which also yielded marginal significance level. CONCLUSION: The present study demonstrated that the use of EMG-biofeedback RABWSTT enhanced the walking performance for SCI subjects and improve cardiopulmonary function. Positive outcomes reflect that RABSTT training may be able to enhance their physical fitness. TRIAL REGISTRATION: The study protocol was approved by the Research Ethics Committee (Kowloon Central/ Kowloon East), Hospital Authority on 6 December 2013, and the Human Subjects Ethics Sub-committee of The Hong Kong Polytechnic University on 15 May 2013, with reference numbers KC/KC-13-0181/ER-2 and HSEARS20130510002 respectively. The study was registered in ClinicalTrials.gov on 20 November 2013, with reference number NCT01989806 .).

Automating a Magnetic 3D Spheroid Model Technology for High-Throughput Screening.

Affordable and physiologically relevant three-dimensional (3D) cell-based assays used in high-throughput screening (HTS) are on the rise in early drug discovery. These technologies have been aided by the recent adaptation of novel microplate treatments and spheroid culturing techniques. One such technology involves the use of nanoparticle (NanoShuttle-PL) labeled cells and custom magnetic drives to assist in cell aggregation to ensure rapid 3D structure formation after the cells have been dispensed into microtiter plates. Transitioning this technology from a low-throughput manual benchtop application, as previously published by our lab, into a robotically enabled format achieves orders of magnitude greater throughput but required the development of specialized support hardware. This effort included in-house development, fabrication, and testing of ancillary devices that assist robotic handing and high-precision placement of microtiter plates into an incubator embedded with magnetic drives. Utilizing a "rapid prototyping" approach facilitated by cloud-based computer-aided design software, we built the necessary components using hobby-grade 3D printers with turnaround times that rival those of traditional manufacturing/development practices at a substantially reduced cost. This approach culminated in a first-in-class HTS-compatible 3D system in which we have coupled 3D bioprinting to a fully automated HTS robotic platform utilizing our novel magnetic incubator shelf assemblies.

Vision-aided brain-machine interface training system for robotic arm control and clinical application on two patients with cervical spinal cord injury.

BACKGROUND: While spontaneous robotic arm control using motor imagery has been reported, most previous successful cases have used invasive approaches with advantages in spatial resolution. However, still many researchers continue to investigate methods for robotic arm control with noninvasive neural signal. Most of noninvasive control of robotic arm utilizes P300, steady state visually evoked potential, N2pc, and mental tasks differentiation. Even though these approaches demonstrated successful accuracy, they are limited in time efficiency and user intuition, and mostly require visual stimulation. Ultimately, velocity vector construction using electroencephalography activated by motion-related motor imagery can be considered as a substitution. In this study, a vision-aided brain-machine interface training system for robotic arm control is proposed and developed. METHODS: The proposed system uses a Microsoft Kinect to detect and estimates the 3D positions of the possible target objects. The predicted velocity vector for robot arm input is compensated using the artificial potential to follow an intended one among the possible targets. Two participants with cervical spinal cord injury trained with the system to explore its possible effects. RESULTS: In a situation with four possible targets, the proposed system significantly improved the distance error to the intended target compared to the unintended ones (p < 0.0001). Functional magnetic resonance imaging after five sessions of observation-based training with the developed system showed brain activation patterns with tendency of focusing to ipsilateral primary motor and sensory cortex, posterior parietal cortex, and contralateral cerebellum. However, shared control with blending parameter α less than 1 was not successful and success rate for touching an instructed target was less than the chance level (= 50%). CONCLUSIONS: The pilot clinical study utilizing the training system suggested potential beneficial effects in characterizing the brain activation patterns.

Chemical Sensing at the Robot Fingertips: Toward Automated Taste Discrimination in Food Samples.

The development of robotic sensors that mimic the human sensing capabilities is critical for the interaction and cognitive abilities of modern robots. Though robotic skin with embedded pressure or temperature sensors has received recent attention, robotic chemical sensors have long been unnoticed due to the challenges associated with realizing chemical sensing modalities on robotic platforms. For realizing such chemically sensitive robotic skin, we exploit here the recent advances in wearable chemical sensor technology and flexible electronics, and describe chemical sensing robotic fingers for rapid screening of food flavors and additives. The stretchable taste-sensing finger electrochemical devices are printed on the robotic glove, which simulates the soft skin, and are integrated with a wireless electronic board for real-time data transmission. The printed middle, index, and ring robotic fingers allow accurate discrimination between sweetness, sourness, and spiciness, via direct electrochemical detection of glucose, ascorbic acid, and capsaicin. The sweet-sensing ability has been coupled with a caffeine-sensing robotic finger for rapid screening of the presence of sugar and caffeine in common beverages. The "sense of taste" chemically sensitive robotic technology thus enables accurate discrimination between different flavors, as was illustrated in numerous tests involving a wide range of liquid and solid food samples. Such realization of advanced wearable taste-sensing systems at the robot fingertips should pave the way to automated chemical sensing machinery, facilitating robotic decision for practical food assistance applications, with broad implications to a wide range of robotic sensing applications.

RASPELD to Perform High-End Screening in an Academic Environment toward the Development of Cancer Therapeutics.

The identification of compounds for dissecting biological functions and the development of novel drug molecules are central tasks that often require screening campaigns. However, the required architecture is cost- and time-intensive. Herein we describe the devices and technologies that comprise a Robotics-Assisted Screening Platform for Efficient Ligand Discovery (RASPELD), which we set up in an academic laboratory. RASPELD provides semi-automated high-end screening, and it can be maintained by graduate students. We demonstrate its successful application in biochemical and cellular screens for the identification and validation of bioactive chemical entities as candidate cancer-relevant inhibitors. Specifically, we examined the interaction between a transcription factor, Nrf2, and its key regulator, Keap1. We also examined drug-resistant mutants of the epidermal growth factor receptor (EGFR). Screening campaigns with more than 30 000 compounds were performed in a reasonable period of time. We identified the molecule RSL6586 as a starting point for hit optimization, which is currently ongoing.

A portable back massage robot based on Traditional Chinese Medicine.

OBJECTIVE: A portable back massage robot which can complete the massage operations such as tapping, kneading and rolling was designed to improve the level of intelligence and massage effect. An efficient full covered path planning algorithm was put forward for a portable back massage robot to improve the coverage. BACKGROUND: Currently, massage robots has become one of important research focuses with the increasing requirements for healthcare. The massage robot is difficult to be widely accepted as there are problems of massage robot in control, structure, and coverage path planning. METHOD: The 3D electromagnetic simulation model was established to optimize electromagnetic force. By analyzing the Traditional Chinese Medicine massage operation and the demands, the path planning algorithm models were established. The experimental platform of the massage robot was built. RESULTS: The simulation results show presented path planning algorithm is suitable for back massage, which ensures that the massage robot traverse the entire back area with improved massage coverage. The tested results show that the massage effect is best when the duty cycle is in the range of 1/8 to 1/2, and the massage force increases with the increase of the input voltage. CONCLUSIONS: The massage robot eventually achieved the desired massage effect, and the proposed efficient algorithm can effectively improve the coverage and promote the massage effect.