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Continuous Prediction of Leg Kinematics during Walking using Inertial Sensors, Smart Glasses, and embedded computing

Continuous Prediction of Leg Kinematics during Walking using...

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ieee international conference on Robotics and Automation (ICRA)

作者： Tsepa, Oleksii Burakov, Roman Laschowski, Brokoslaw Mihailidis, Alex Igor Sikorsky Kyiv Polytech Inst Dept Math Methods Syst Anal Kiev Ukraine Univ Toronto Dept Comp Sci Toronto ON Canada Natl Univ Kyiv Mohyla Acad Dept Math Kiev Ukraine Univ Toronto Dept Mech & Ind Engn Toronto ON Canada Univ Hlth Network Toronto Rehabil Inst Toronto ON Canada Univ Toronto Inst Biomed Engn Toronto ON Canada

ISBN: (纸本)9798350323658

Unlike traditional hierarchical controllers for robotic leg prostheses and exoskeletons, continuous systems could allow persons with mobility impairments to walk more naturally in real-world environments without requiring high-level switching between locomotion modes. To support these next-generation controllers, we developed a new system called KIFNet (Kinematics and Image Fusing Network) that uses lightweight and efficient deep learning models to continuously predict the leg kinematics during walking. We tested different sensor fusion methods to combine kinematics data from inertial sensors and computer vision data from smart glasses and found that adaptive instance normalization achieved the lowest RMSE predictions for knee and ankle joint kinematics. We also deployed our model on an embedded device. Without inference optimization, our model was 20 times faster than the previous state-of-the-art and achieved 20% higher prediction accuracies, and during some locomotor activities like stair descent, decreased RMSE up to 300%. With inference optimization, our best model achieved 125 FPS on an NVIDIA Jetson Nano. These results demonstrate the potential to build fast and accurate deep learning models for continuous prediction of leg kinematics during walking based on sensor fusion and embedded computing, therein providing a foundation for real-time continuous controllers for robotic leg prostheses and exoskeletons.

关键词： Kinematics

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4966| real-time implementation of nonlinear controllers and predictors for off-road vehicle dynamics on embedded systems 21

4966| Real-time implementation of nonlinear controllers and ...

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21st international and 12th Asia-Pacific Regional conference of the international Society for Terrain-Vehicle systems, ISTVS 2024

作者： Peenze, Andries J. Guthrie, A. Glenn Els, P. Schalk Department of Mechanical and Aeronautical Engineering University of Pretoria Pretoria South Africa

ISBN: (纸本)9781942112570

The vehicle dynamics of off-road vehicles tend to be non-linear concerning suspension dynamics, tyre-terrain interactions, and subsystem kinematics. These non-linearities limit the performance of control due to the need for linearization to enable real-time application. Implementing real-time non-linear controllers and prediction methods on embedded systems, such as the dSPACE MicroAutoBox 2 (MABX2), is challenging due it being computationally intensive. This challenge is particularly pronounced for controllers utilizing optimization schemes with non-linear solvers to solve optimal control problems. In this paper, we explore the possibility of embedding non-linear optimal controllers and predictors on a MABX2 for real-time applications. The implementation uses CasADi to formulate and code-generate the problems, and then executes this code using S-Functions in Simulink or off-loading the execution to a powerful external computer. We discuss the limitations and special considerations for these real-time implementations for off-road vehicle applications. © 2024 international Society for Terrain-Vehicle systems. All rights reserved.

关键词： Off road vehicles

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Deeply Quantized Neural Networks: Enabling In Sensor computing 1

Deeply Quantized Neural Networks: Enabling In Sensor Computi...

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1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart systems, ISTEMS 2024

作者： Chandna, Manav Suneja, Saumya Luise, Loris Caruso, Fabio Vito Noronha, Ian Wilfred Dabral, Hem Dutt Singh, Surinder-Pal System Research and Applications STMicroelectronics Greater Noida India System Research and Applications STMicroelectronics Colleoni Italy

ISBN: (纸本)9798350384246

AI on the edge as a paradigm promises unparalleled energy efficiency and data privacy among other benefits. This has resulted in a rising trend of approaches to bring AI computing near or embedded directly inside a sensing element. In this paper, we describe a workflow of executing contemporary deep learning applications along with schemes to overcome the challenges of implementing this class of applications in a resource-constrained computing environment. We demonstrate the performance on a state-of-the-art AI-capable MEMS IMU sensor product from STMicroelectronics called the Intelligent Sensor Processing Unit (ISPU) © 2024 ieee.

关键词： Deep learning

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Development of a Test Automation Framework based on a Comparison of different Approaches for Test Automation in the embedded systems Area 7

Development of a Test Automation Framework based on a Compar...

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ieee 7th international conference on Industrial Cyber-Physical systems (ICPS)

作者： Arthofer, Stefan Wilker, Stefan Sauter, Thilo TU Wien Vienna Austria Siemens AG Osterreich Vienna Austria TU Wien Inst Comp Technol Vienna Austria Univ Continuing Educ Krems Dept Integrated Sensor Syst Krems An Der Donau Austria

ISBN: (纸本)9798350363029;9798350363012

embedded systems have become an integral part of our everyday lives. Devices are increasingly connected, especially in the Internet of Things (IoT) environment. The interfaces between the devices require high-quality software and hardware to ensure reliable communication between them. This vital interconnection also leads to systems becoming increasingly complex, and therefore, the complexity of the tests increases. Therefore, such tests can usually only be done by means of automation, as manual testing is very time-consuming and cost-intensive. A System Under Test (SUT) has different requirements depending on the application area and the functionality, which is why different test automation approaches exist. This work reviews possible approaches for test automation of embedded devices and compares them with each other based on different criteria. Based on this comparison and in cooperation with Siemens Smart Infrastructure EA, a test automation system called Kubrotest was designed. To verify the test automation in a real usecase, Kubrotest is used to test a Siemens SICAM 8 series device.

关键词： test automation embedded systems robot framework ansible ranorex

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A Wearable System for Diagnosing and Monitoring the Intensity of Stuttering 6

A Wearable System for Diagnosing and Monitoring the Intensit...

引用

2024 international conference on Smart systems and Technologies

作者： Susac, Petar Gudan, Nikola Kuk, Petra Salamun, Karla Dzapo, Hrvoje Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Univ Zagreb Fac Educ & Rehabil Sci Zagreb Croatia

ISBN: (纸本)9798350386394;9798350386400

We present a battery-powered wearable system that is able to identify the three basic types of speech disfluencies found in people who stutter: blocks, prolongations, and repetitions. Such a system could be used to aid speech pathology clinicians by performing automated diagnosis of stuttering or monitoring the progress of speech therapy, tasks that are currently time-consuming and produce potentially unreliable results. The system uses a deep learning model trained on the SEP-28k dataset and deployed on a microcontroller. It performs speech audio acquisition and model inference in real time and stores the inference results to non-volatile memory. Once stored, the results can be further analyzed on a PC and presented to the clinician. Our deep learning model achieved a classification accuracy of 65%, 71%, and 64% for blocks, prolongations, and repetitions, respectively. We discuss the possible applications of this system in speech disorder diagnosis and therapy as well as potential improvements.

关键词： audio deep learning embedded systems speech stuttering wearable

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Classification and Featurization of Motherboard Components Using YOLO Through Machine Learning

Classification and Featurization of Motherboard Components U...

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2024 ieee international conference on computing, applications and systems, COMPAS 2024

作者： Rahman Fahim, Md. Imadur Mahazabin Senthia, Sabrina Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

The role of technology is vital and can be observed through the 5th industrial revolution. As a matter of fact, the impact is so severe, it can be felt almost everywhere. As technology advances, one of the most promising frontiers lies in the realm of Artificial Intelligence (AI), with machine learning at its forefront. Object detection is a crucial such part with applications in robotics and image analysis. This paper presents a comprehensive study on vision AI, focusing on the detection of motherboard components utilizing the YOLOv8 object detection framework. The results demonstrate the effectiveness and real time effectiveness of YOLOv8 in terms of motherboard object detection. Simultaneously, this work validates the feasibility of employing YOLOv8 for in-depth analysis and object detection on various circuit boards. This will allow many automated devices to repair circuit boards with the help of artificial intelligence. Consequently, allowing robots to detect flaws and repair integrated hardware components in situations considered difficult for humans. © 2024 ieee.

关键词： Robot vision

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A real-time Detection of Patient's Aggressive Behaviors on a Smart Sensor 67

A Real-Time Detection of Patient's Aggressive Behaviors on a...

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67th ieee international Midwest Symposium on Circuits and systems (MWSCAS)

作者： Inoue, Yudai Moshnyaga, Vasily G. Fukuoka Univ Dept Elect Engn & Comp Sci Fukuoka 8140180 Japan

ISBN: (纸本)9798350387186;9798350387179

Patient aggression in older people with dementia is one of the most challenging problems of medical care in today's nursing homes. Existing wireless autography-based technologies help caregivers to collect data related to behavioral and psychological symptoms of dementia. However, their inability to process the data in real-time reduces the efficiency of patient assessment and affects the quality of care. In this paper we present a computationally inexpensive technique that identifies aggressive behaviors on a sensor that measures the wrist acceleration of the dominant patient hand. Experiments show that the technique effectively detects aggressive behaviors in real-time and significantly reduces the wireless data transmissions.

关键词： Dementia Agitation Aggressive Behavior Risk assessment Machine learning Sensor systems and applications

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XANDAR: An X-by-Construction Framework for Safety, Security, and real-time Behavior of embedded Software systems

XANDAR: An X-by-Construction Framework for Safety, Security,...

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27th Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Doerr, Tobias Schade, Florian Becker, Juergen Keramidas, Georgios Petrellis, Nikos Kelefouras, Vasilios Mavropoulos, Michail Antonopoulos, Konstantinos Antonopoulos, Christos P. Voros, Nikolaos Ahlbrecht, Alexander Zaeske, Wanja Janson, Vincent Noeldeke, Phillip Durak, Umut Panagiotou, Christos Karadimas, Dimitris Adler, Nico Reichmann, Clemens Sailer, Andreas Weber, Raphael Wilhelm, Thomas Gabler, Wolfgang Weiden, Katrin Recasens, Xavier Anzuela Sezer, Sakir Siddiqui, Fahad Khan, Rafiullah McLaughlin, Kieran Tasdemir, Sena Yengec Sonigara, Balmukund Hui, Henry Viguer, Esther Soriano Suarez, Aridane Alvarez Gallego, Vicente Nicolau Alcobendas, Manuel Munoz Tello, Miguel Masmano Karlsruhe Inst Technol Karlsruhe Germany Univ Peloponnese Tripoli Greece German Aerosp Ctr DLR Inst Flight Syst Braunschweig Germany AVN Innovat Technol Solut Ltd Limassol Cyprus Vector Informat GmbH Stuttgart Germany Bayer Motoren Werke Aktiengesell Munich Germany Queens Univ Belfast Antrim North Ireland Fent Innovat Software Solut SL Valencia Spain Aristotle Univ Thessaloniki Thessaloniki Greece

ISBN: (纸本)9798350348606;9783981926385

The safe and secure implementation of increasingly complex features is a major challenge in the development of autonomous and distributed embedded systems. Automated design-time procedures that guarantee the fulfillment of critical system properties are a promising approach to tackle this challenge. In the European project XANDAR, which took place from 2021 to 2023, eight partners developed an X-by-Construction (XbC) design framework to support developers in the creation of embedded software systems with certain safety, security, and real-time properties. The design framework combines a model-based toolchain with a hypervisor-based runtime architecture. It targets modern high-performance hardware, facilitates the integration of machine learning applications, and employs a library of trusted safety and security patterns to reduce the implementation and verification effort. This paper describes the concepts developed during the project, the prototypical implementation of the design framework, and its application in both an automotive and an avionics use case.

关键词： X-by-Construction model-based development real-time systems safety-critical systems hypervisors

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Cloning Image Features from Software Environment to a Hardware Media for Machine Vision applications 21

Cloning Image Features from Software Environment to a Hardwa...

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21st international Multi-conference on systems, Signals and Devices, SSD 2024

作者： Khalil, Mazin Rejab Hasso, Ezideen A Ali, Qusay Hamid Tishk International University Mechatronics Engineering Department Erbil Iraq

ISBN: (纸本)9798350374131

The paper outlines a methodology for crafting an embedded processor system to replicate image features computed within a mat lab software environment onto a hardware-based processor system. The proposed approach depends on computing the features of the hand gesture images using Histograms of Oriented Gradients algorithm in the software media to be cloned to a hardware environment represented by an embedded processor system. embedded Design Techniques were used to construct the processor system to be configured on Spartan 3E slice with DDR-SDRAM and EEPROM external memories connected to the processor via multiport memory controller embracing the processor local bus. The system saves the cloned features in the EEPROM for hardware image recognition applications. The designed configuration allows to be tailored with a robot camera circuitry for machine vision systems. © 2024 ieee.

关键词： Object recognition

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CADET: Control-Aware Dynamic Edge computing for real-time Target Tracking in UAV systems 48

CADET: Control-Aware Dynamic Edge Computing for Real-Time Ta...

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48th ieee international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Florenzan Reyes, Luis Felipe Smarra, Francesco D'Innocenzo, Alessandro Levorato, Marco University of l'Aquila Department of Information Engineering Computer Science and Mathematics AQ 67100 Italy University of California Department of Computer Science IrvineCA92697 United States

ISBN: (纸本)9781728163277

The autonomous operations of unmanned aerial vehicles (UAV) necessitate the real-time analysis of information-rich signals, such as camera and LiDAR feeds, where the analysis algorithms often take the form of extremely complex deep neural networks (DNN). The continuous execution of such models onboard the UAV imposes a considerable resource consumption (e.g., energy), while offloading the execution of the models to edge servers requires the transmission of the input signals over capacity constrained, time-varying, wireless channels. In this paper, we propose an innovative approach - CADET - to control where sensor signals are processed in the system. In addition to traditional features and measures, such as channel state, energy consumption and channel usage, CADET makes dynamic task routing decisions - local computing vs edge computing - based on the state of the flight controller. The proposed methodology is based on Markov jump switched linear systems, where an embedded filter predicts and controls the state of the joint motion/computing dynamics. © 2023 ieee.

关键词： Edge computing

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