Vision Sensor Research Articles

Research on the Optimization Method of Visual Sensor Calibration Combining Convex Lens Imaging with the Bionic Algorithm of Wolf Pack Predation.

To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera are regarded as the search targets of the bionic algorithm of Wolf Pack Predation, and the reprojection error of the calibration results is used as the fitness evaluation criterion of the bionic algorithm of Wolf Pack Predation. The goal of optimizing camera calibration parameters is achieved by iteratively searching for a solution that minimizes the fitness value. To overcome the drawback that the bionic algorithm of Wolf Pack Predation is prone to fall into local optimal, a reverse learning strategy based on convex lens imaging is introduced to transform the current optimal individual and generate a series of new individuals with potential better solutions that are different from the original individual, helping the algorithm out of the local optimum dilemma. The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang's calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. The results indicate that calibration accuracy, stability, and robustness are significantly improved with the optimization method based on the CLI-WPP, in comparison to the existing commonly used optimization algorithms.

Double-Fluorescent Response Spiropyran-Functionalized Graphitic Carbon Nitride Solvent Decoder and Nanofiber Mat-Based Visual Colorimetric Sensor for Humidity Detection within Grain Heaps.

Spiropyran-functionalized graphitic carbon nitride (MCH@g-C3N4) in acidic conditions was prepared for the first time. MCH@g-C3N4 exhibited dual-wavelength fluorescence emission with two distant bands and distinct solvatochromic behavior owing to the different molecular structures of spiropyran. A three-dimensional organic solvent decoder was fabricated for solvent identification based on the emission wavelength and fluorescence quantum yield of MCH@g-C3N4 in different polar solvents. The ratiometric fluorescence sensing of the water content in organic solvent was realized using water to induce the structural change of spiropyran. A solid-phase MCH@g-C3N4/polyvinylpyrrolidone (PVP) nanofiber mat-based sensor was prepared via cospinning MCH@g-C3N4 with hydrophilic PVP. A fluorescent humidity sensor was fabricated using a commercial syringe equipped with a stainless-steel needle (catcher) to obtain the air inside the grain heap, which enabled the accurate location of the sampling spot. The space between the syringe and piston was used as an enclosed sensing space. Thus, a simple, accurate, and intuitive visual colorimetric method for the internal humidity of a grain heap was realized by analyzing the recorded images.

Enhanced peroxidase-like activity via Pt loading of Ni-MOF for colorimetric and smartphone-based bimodal determination of antioxidant capacity in aging cells

Total antioxidant capacity (TAC) serves as a vital indicator for evaluating cell senescence. Here, we synthesized a platinum-loaded nickel-based metal–organic framework material (Ni-MOF@Pt) via a solvothermal method for bimodal determination of TAC in aging cells. The Pt nanoparticle incorporation substantially enhanced the peroxidase-like catalytic activity of Ni-MOF. Ni-MOF@Pt exhibited a strong affinity for 3,3′,5,5′-tetramethylbenzidine (TMB) and H2O2, with lower Km values compared to horseradish peroxidase (HRP). The Ni-MOF@Pt-TMB-H2O2 system was successfully applied for detecting antioxidants, including glutathione (GSH), ascorbic acid (AA), and cysteine (Cys) with impressive detection limits (121 nM for GSH, 119 nM for AA, 72 nM for Cys), highlighting the high sensitivity of this system. Additionally, a smartphone-assisted visual detection sensor was established using Ni-MOF@Pt for rapid, sensitive, and on-site TAC detection. Moreover, our established Ni-MOF@Pt method effectively determined the TAC of aging cells and demonstrated its ability to differentiate between normal, senescent, and cancerous cells. This research offers novel insight on fabricating nanozymes with potent peroxidase-like properties and manifests their practicality in TAC analysis.

Event-driven nearshore and shoreline coastline detection on SpiNNaker neuromorphic hardware

Abstract Coastline detection is vital for coastal management, involving frequent observation and assessment to understand coastal dynamics and inform decisions on environmental protection. 
Continuous streaming of high-resolution images demands robust data processing and storage solutions to manage large datasets efficiently, posing challenges that require innovative solutions for real-time analysis and meaningful insights extraction.
This work leverages low-latency event-based vision sensors coupled with neuromorphic hardware in an attempt to decrease a two-fold challenge, reducing the computational burden to ~0.375 mW whilst obtaining a coastline detection map in as little as 20 ms.
The proposed Spiking Neural Network (SNN) runs on the SpiNNaker neuromorphic platform using a total of 18040 neurons reaching 98.33% accuracy.
The model has been characterised and evaluated by computing the accuracy of Intersection over Union (IoU) scores over the ground truth of a real-world coastline dataset across different time windows. The system's robustness was further assessed by evaluating its ability to avoid coastline detection in non-coastline profiles and funny shapes, achieving a success rate of 97.3%.

Drone-DETR: Efficient Small Object Detection for Remote Sensing Image Using Enhanced RT-DETR Model.

Performing low-latency, high-precision object detection on unmanned aerial vehicles (UAVs) equipped with vision sensors holds significant importance. However, the current limitations of embedded UAV devices present challenges in balancing accuracy and speed, particularly in the analysis of high-precision remote sensing images. This challenge is particularly pronounced in scenarios involving numerous small objects, intricate backgrounds, and occluded overlaps. To address these issues, we introduce the Drone-DETR model, which is based on RT-DETR. To overcome the difficulties associated with detecting small objects and reducing redundant computations arising from complex backgrounds in ultra-wide-angle images, we propose the Effective Small Object Detection Network (ESDNet). This network preserves detailed information about small objects, reduces redundant computations, and adopts a lightweight architecture. Furthermore, we introduce the Enhanced Dual-Path Feature Fusion Attention Module (EDF-FAM) within the neck network. This module is specifically designed to enhance the network's ability to handle multi-scale objects. We employ a dynamic competitive learning strategy to enhance the model's capability to efficiently fuse multi-scale features. Additionally, we incorporate the P2 shallow feature layer from the ESDNet into the neck network to enhance the model's ability to fuse small-object features, thereby enhancing the accuracy of small object detection. Experimental results indicate that the Drone-DETR model achieves an mAP50 of 53.9% with only 28.7 million parameters on the VisDrone2019 dataset, representing an 8.1% enhancement over RT-DETR-R18.

Advancements in Autonomous Mobile Robot: A Holistic Review of Obstacle Avoidance Methods

The emergence of AMRs has altered our perspective and relationship with automation. At the heart of this transition is navigation and obstacle avoidance, both of which are important needs for deploying AMRs in a variety of scenarios. This comprehensive review looks at the latest advances in navigation and collision avoidance for AMRs, including a wide range of modern techniques and methodologies, algorithms, and technologies that aim to improve functionality. The study provides a detailed analysis of known approaches, such as rule-based approaches, potential fields, reactive navigation systems as behavior systems, and path-following algorithms, that have been developed to address the difficulty in practice. In contrast, technological advancements in machine learning, computer vision sensor fusion, and SLAM techniques, as well as edge computing, are reviewed in light of their unprecedented impact on AMR navigation. Global and local techniques are tackled using universal worldwide optics as well as national adaptations that reveal the unique characteristics of individual countries. The Data Analysis and Processing section emphasizes the importance of technologies that define AMR performance. Due to the constraints imposed by previous studies, it is clear that additional research is required to focus on closing gaps in controlled environments and using standard benchmarks; sensor heterogeneity issues; and practical implementation of theoretical aspects. In a nutshell, this review provides a map of the complex world of AMR navigation and obstacle avoidance. Its primary purpose is to contribute to the continuing debate, promote innovation, and suggest new research avenues in a fast-changing world of autonomous mobile robotics that breaks down traditional deployment constraints.

Full surface profile measurement of a small sized ball based on a stitching measurement method by reversal clamping

For the full surface profile measurement of a small sized ball, traditional measurement methods inevitably have blind measurement areas since clamping part cannot be directly measured. To address the challenge of measuring the full surface, this paper proposed a reversal clamping measurement method based on a bidirectional vacuum adsorption system. The position of the measuring part and the clamping part was exchanged by reversal clamping the workpiece, and the full surface was obtained by stitching two measurement results before and after reversal clamping process. Two visual sensors were used to monitor the reversal clamping processes, and the error model was established to separate the alignment and scanning errors. Experiments were performed with the measurement system and a ball with diameter of 4 mm, the feasibility of the full surface measurement was verified to be with a measuring accuracy of hundred nanometer-level.

Artificial Amacrine Retinal Circuits.

Event-based imaging represents a new paradigm in visual information processing that addresses the speed and energy efficiency shortcomings inherently present in the current complementary metal oxide semiconductor-based machine vision. Realizing such imaging systems has previously been sought using very large-scale integration technologies that have complex circuitries consisting of many photodiodes, differential amplifiers, capacitors, and resistors. Here, we demonstrate that event-driven sensing can be achieved using a simple one-resistor, one-capacitor (1R1C) circuit, where the capacitor is modified with colloidal quantum dots (CQDs) to have a photoresponse. This sensory circuit emulates the motion-tracking function of the biological retina, in which the amacrine cells in the bipolar-to-ganglion synaptic pathway produce a transient spiking signal only in response to changes in light intensity but remain inactive under constant illumination. When extended to a 2D imaging array, the individual sensors work independently and output signals only when a change in the light intensity is detected; hence, the concept of the frame in image processing is thereby removed. In this work, we present the fabrication and characterization of a CQD photocapacitor-based 1R1C circuit that has a spectral response at 1550 nm in the short-wave infrared (SWIR). We report on the key performance parameters including peak responsivity, noise, and optical noise equivalent power and discuss the operating mechanism that is responsible for spiking responses in these artificial retinal circuits. The present work sets the foundation for expanding the bioinspired vision sensor capability toward midwave infrared (MWIR) and long-wave infrared (LWIR) spectral regions that are invisible to human eyes and mainstream semiconductor technologies.

Development of an Adaptive Force Control Strategy for Soft Robotic Gripping

Using soft materials in robotic mechanisms has become a common solution to overcome many challenges associated with the rigid bodies frequently used in robotics. Compliant mechanisms allow the robot to adapt to objects and perform a broader range of tasks, unlike rigid bodies that are generally designed for specific applications. However, soft robotics presents its own set of challenges in both design and implementation, particularly in sensing and control. These challenges are abundant when dealing with the force control problem of a compliant gripping mechanism. The ability to effectively regulate the applied force of a gripper is a critical task in many control operations, as it allows the precise manipulation of objects, which drives the need for enhanced force control strategies for soft or flexible grippers. Standard sensing techniques, such as motor current monitoring and strain-based sensors, add complexities and uncertainties when establishing mathematical models of soft grippers to the required gripping forces. In addition, the soft gripper creates a complex non-linear system, compounded by adding an adhesive-type sensor. This work develops a unique visual force sensor trained on synthetic data generated using finite element analysis (FEA) and implemented by integrating a non-linear model reference adaptive controller (MRAC) to control gripping force on a fixed 6-DOF robot. The robot can be placed on a mobile platform to perform various tasks. The virtual FEA sensor and controller, combined, are termed virtual reference adaptive control (VRAC). The VRAC was compared to other methods and achieved comparable control sensing and control performance while reducing the complexity of the sensor requirements and its integration. The VRAC strategy effectively controlled the gripping force by driving the dynamics to match the desired performance after a limited amount of training cycles. The controller proposed in this work was designed to be generally applicable to most objects that the gripper will interact with and easily adaptable to a wide variety of soft grippers.

Tightly Coupled Visual–Inertial Fusion for Attitude Estimation of Spacecraft

The star sensor boasts the highest accuracy in spacecraft attitude measurement. However, it is vulnerable to disturbances, including high-dynamic motion, stray light, and various in-orbit environmental factors. These disruptions may lead to a significant decline in attitude accuracy or even abnormal output, potentially inducing a state of disorientation in the spacecraft. Thus, it is usually coupled with a high-frequency gyroscope to compensate for this limitation. Nevertheless, the accuracy of long-term attitude estimation using a gyroscope decreases due to the presence of bias. We propose an optimization-based tightly coupled scheme to enhance attitude estimation accuracy under dynamic conditions as well as to bolster the star sensor’s robustness in cases like lost-in-space. Our approach commenced with visual–inertial measurement preprocessing and estimator initialization. Subsequently, the enhancement of attitude and bias estimation precision was achieved by minimizing visual and inertial constraints. Additionally, a keyframe-based sliding window approach was employed to mitigate potential failures in visual sensor measurements. Numerical tests were performed to validate that, under identical dynamic conditions, the proposed method achieves a 50% improvement in the accuracy of yaw, pitch, and roll angles in comparison to the star sensor only.

A novel approach for large-scale characterization of residential cooking-generated PM with computer vision and low-cost sensors

Cooking is one of the major sources of indoor particulate matter (PM), which poses significant health risks and is a severe health hazard. Current studies lack an economical and effective analytical framework for quantifying inhalable particles (PM10) and fine particulate matter (PM2.5) from residential cooking activities on a large scale under real-world scenarios. This study bridges this gap by employing computer vision (CV) technology and readily available sensors. We collected data over a month in real-world settings, including cooking videos and air quality data (indoor PM10, PM2.5, CO2, temperature, relative humidity, and outdoor PM10 and PM2.5 concentrations). To classify high-emission (pan-frying, stir-frying, deep-frying) and low-emission (stewing, steaming, boiling, non-cooking) activities, we developed and validated a robust CV model named “Cooking-I3D.” This model leverages a pre-trained Two-Stream Inflated 3D ConvNet (I3D) architecture. We then assessed the efficacy of the CV-predicted cooking method in PM characterization using a first-order multivariate autoregressive model, controlling for environmental factors. The Cooking-I3D model achieved exceptional performance, boasting an accuracy of 95 % and an Area Under the Curve (AUC) of 0.98. Our results indicate that a single 6-minute high-emission cooking event triggers a 21–25 % increase in indoor PM concentrations and a 23–24 % increase in the indoor/outdoor ratio, with relative errors in these estimates ranging from 10 to 21 %. This innovative method offers a powerful tool for long-term assessment of cooking-related indoor air pollution and facilitates precision exposure assessment in human health studies.

Deep learning based enhanced secure emergency video streaming approach by leveraging blockchain technology for Vehicular AdHoc 5G Networks

VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology.

Eu(III)-functionalized metal–organic framework as a visual ratiometric fluorescence sensor for rapid discrimination and detection of multiple tetracyclines in agricultural products

The misuse of oxytetracycline (OTC), doxycycline (DOX), tetracycline (TC), and chlortetracycline (CTC) poses a serious threat to human health and it calls for rapid and visual methods. In this study, a novel ratio fluorescent sensor was constructed by simply combining Eu(III) with IRMOF-3. Spectral characterization and DFT theoretical calculations revealed different mechanism of interactions between the MOF-based sensor and different tetracyclines in detail. PCA analysis amplified these differences, enabling the simultaneous identification and differentiation of individual, binary or ternary mixtures of multiple tetracyclines. Subsequently, a rapid fluorescence sensing method (<5 min) for detecting OTC, DOX, TC, and CTC was established with low detection limits of 70, 75, 40, and 168 nM, respectively, which are all below the maximum residue limits by the European Union and the U.S. Food and Drug Administration regulations. A portable visual test strip was also conducted based smartphone with low detection limits of 1 μM, exhibiting an obvious color spectrum transition from blue to purple and finally to pink. Furthermore, the sensor was applied successful to pig feed, milk and veterinary drugs. The results showed good agreement with HPLC-MS/MS analysis.

Determination of refractive index of liquids using a machine vision sensor and a programmable device

Abstract In this implementation, it is proposed a measurement method and/or educational-demonstrative resource that determines the refractive index of a liquid. A method can be found in the literature which relates the diameter of an object to the diameter of its amplified image when it is introduced in a container filled with the liquid on an unknown refractive index. Therefore, this proposal is based on measuring the diameter of the amplified image related to a cylindrical object immersed in a beaker, by means of using the color recognition function of an artificial intelligence camera; while the data processing will be carried out with the use of a micro:bit board. Finally, the result will be displayed in a 128 × 64 OLED screen. According to the characteristics and materials used in the proposal, it can be perfectly applied in undergraduate and graduate levels in the field of Physics or Engineering. Furthermore, the proposed device does not require an external computer or specialized software for use, thus also offering easy assembly, portability and automation.

Visual Sensor Array for Multiple Aromatic Amines via Specific Ascorbic Acid Oxidase Mimic Triggered Schiff-Base Chemistry.

Redox nanozymes have exhibited various applications in recognizing environmental pollutants but not aromatic amines (a type of typical pollutant). Herein, with Cu2+ as a node and tryptophan (Trp) as a linker, Cu-Trp as a specific ascorbic acid oxidase mimic was synthesized, which could catalyze ascorbic acid (AA) oxidation to dehydroascorbic acid (DHAA). Alternatively, with other natural amino acids as linkers to synthesize Cu-based nanozymes, such catalytic performances are also observed. The as-produced DHAA could react with o-phenylenediamine (OPD) and its derivatives (2,3-naphthalene diamine (NDA), 4-nitro-o-phenylenediamine (4-NO2-OPD), 4-fluoro-o-phenylenediamine (4-F-OPD), 4-chloro-o-phenylenediamine(4-Cl-OPD), and 4-bromo-o-phenylenediamine(4-Br-OPD)) to form a Schiff base and emit fluorescence. Based on the results, with Cu-Trp + AA and Cu-Arg (with arginine (Arg) as a linker) + AA as two sensing channels and extracted red, green, and blue (RGB) values from emitted fluorescence as read-out signals, a visual sensor array was constructed to efficiently distinguish OPD, NDA, 4-NO2-OPD, 4-F-OPD, 4-Cl-OPD, and 4-Br-OPD as low as 10 μM. Such detecting performance was further confirmed through discriminating binary, ternary, quinary, and senary mixtures with various concentration ratios, recognizing 18 unknown samples, and even quantitatively analyzing single aromatic amine. Finally, the discriminating ability was further validated in environmental waters, providing an efficient assay for large-scale scanning levels of multiple aromatic amines.

Hybrid Sensor Fusion Mixed with Reinforcement Learning in Autonomous Dual-Arm Lifting Tasks Performed by Humanoid Robots

Humanoid robots often struggle with tasks such as lifting objects due to the complexity involved in identifying contact points, applying the correct force, and tracking task progress. We propose an integrated solution that leverages the dual-arm capability of humanoids and utilizes sensor fusion from vision and force sensors. Our system employs a computer vision algorithm to detect and characterize object properties (shape, size, position, orientation) and differentiate between parallel and non-parallel bi-manipulation tasks. The controller then identifies optimal contact points for the end effectors, generating trajectories fed into a closed-loop controller using force feedback. For parallel bi-manipulation, momentum cancellation is achieved through sensor fusion. For non-parallel surfaces, a reinforcement learning algorithm determines the appropriate lifting force to prevent slippage using only two contact points. Experimental validation on a real humanoid platform demonstrates the effectiveness of our approach in autonomously lifting objects, regardless of contact surface configuration. This advancement significantly enhances the reliability and versatility of humanoid robots in performing complex manipulation tasks, contributing to their practical deployment in human-oriented environments.

Digital modelling method of coal-mine roadway based on millimeter-wave radar array

The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway.

Design of hexapod robot equipped with omnidirectional vision sensor for defect inspection of pipeline’s inner surface

Abstract Defect detection of inner surface of precision pipes is a crucial aspect of ensuring production safety. Currently, pipeline defect detection primarily relies on recording video for manual recognition, with urgent need to improve automation, quantification and accuracy. This paper presents a hexapod in-pipe robot with carrying capacity designed to transport the omnidirectional vision sensor to specified location within unreachable pipelines. The feasibility of the robot’s mechanical design and sensor load-carrying module is analyzed using theory calculations, motion simulations and finite element method. To address the challenges of small pixel ratio and weak background changes in panoramic images, a tiny defect segmentor (TDS) based on ResNet is proposed for detecting tiny defects on the inner surface of pipelines. The hardware and software systems are implemented, and the motion performance of the pipeline robot is validated through experiments. The results demonstrate that the robot achieves stable movement at a speed of over 0.1m/s and can adapt to pipe diameter ranging from of 110 to 130 mm. The novelty of the robot lies in providing stable control of the loaded vision sensor, with control precision of the rotation angle and the displacement recorded at 1.84% and 0.87%, respectively. Furthermore, the proposed method achieves a detection accuracy of 95.67% for tiny defects with a diameter less than 3 mm and provides defect location information. This pipeline robot serves as an essential reference for development of in-pipe 3D vision inspection system.&amp;#xD;

BrainQN: Enhancing the Robustness of Deep Reinforcement Learning with Spiking Neural Networks

As the third‐generation network succeeding artificial neural networks (ANNs), spiking neural networks (SNNs) offer high robustness and low energy consumption. Inspired by biological systems, the limitations of low robustness and high‐power consumption in deep reinforcement learning (DRL) are addressed by introducing SNNs. The Brain Q‐network (BrainQN) is proposed, which replaces the neurons in the classic Deep Q‐learning (DQN) algorithm with SNN neurons. BrainQN is trained using surrogate gradient learning (SGL) and ANN‐to‐SNN conversion methods. Robustness tests with input noise reveal BrainQN's superior performance, achieving an 82.14% increase in rewards under low noise and 71.74% under high noise compared to DQN. These findings highlight BrainQN's robustness and superior performance in noisy environments, supporting its application in complex scenarios. SGL‐trained BrainQN is more robust than ANN‐to‐SNN conversion under high noise. The differences in network output correlations between noisy and original inputs, along with training algorithm distinctions, explain this phenomenon. BrainQN successfully transitioned from a simulated Pong environment to a ball‐catching robot with dynamic vision sensors (DVS). On the neuromorphic chip PAICORE, it shows significant advantages in latency and power consumption compared to Jetson Xavier NX.

Assessing Usability and Feasibility of Using Computer Vision and Smart Sensor to Design a Digital Rehabilitation System, DigiHand for Post-stroke Use

Abstract Date Presented 03/22/24 This presentation will discuss the feasibility and usability of a novel digital system that combines computer vision with smart sensors to measure upper limb functional movement performance and guide the user during their home exercise program. Primary Author and Speaker: Sutanuka Bhattacharjya Contributing Authors: Ashwin Ashok