A Wearable, Patient-Adaptive Freezing of Gait Detection System for Biofeedback Cueing in Parkinson's Disease

Mikos, Val; Heng, Chun-Huat; Tay, Arthur; Yen, Shih-Cheng; Chia, Nicole Shuang Yu; Koh, Karen Mui Ling; Tan, Dawn; Au, Wing Lok

doi:10.1109/tbcas.2019.2914253

Cited by 43 publications

(83 citation statements)

References 34 publications

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“…Mikos et al [70] 2019 Demonstrate the integration of an FoG detection system into a single sensor node.…”

Section: Reference Year Objectivementioning

confidence: 99%

“…Calibration procedures to address this problem have been proposed, such as static pose calibration, requiring the user to take on specific poses, functional calibration, requiring the user to perform movements around defined axes, and technical calibration, requiring manual alignment with respect to the bone structure [85,[87][88][89]. These procedures still have potentially large human-induced errors and researchers have started to study ways to integrate machine learning and deep learning techniques to help improve inaccuracies [45,46,51,62,70].…”

Section: Sensor-to-segment Alignmentmentioning

confidence: 99%

“…If a wearable sensor system doesn't have the capability of running the algorithms locally, servers are preferred since they have a large amount of storage space, processing power, and energy capabilities that allow complex data and algorithms. This approach is really common when machine learning techniques are used because these techniques often require high computational and processing power in order to train the models [45,46,61,62,70]. Systems connected to smartphones can run the algorithms locally if the complexity of the data and algorithms allow it, which depends on the device's limitations of storage, battery, and processing power.…”

Section: Processingmentioning

confidence: 99%

“…Another way to avoid setting thresholds is by using machine learning techniques. By using these techniques, the algorithms are trained to learn the most optimal threshold for a specific problem or measurement [51,[60][61][62]66,70,85].…”

Section: Thresholdsmentioning

confidence: 99%

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Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

2019

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More than 8.6 million people suffer from neurological disorders that affect their gait and balance. Physical therapists provide interventions to improve patient’s functional outcomes, yet balance and gait are often evaluated in a subjective and observational manner. The use of quantitative methods allows for assessment and tracking of patient progress during and after rehabilitation or for early diagnosis of movement disorders. This paper surveys the state-of-the-art in wearable sensor technology in gait, balance, and range of motion research. It serves as a point of reference for future research, describing current solutions and challenges in the field. A two-level taxonomy of rehabilitation assessment is introduced with evaluation metrics and common algorithms utilized in wearable sensor systems.

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“…Mikos et al [70] 2019 Demonstrate the integration of an FoG detection system into a single sensor node.…”

Section: Reference Year Objectivementioning

confidence: 99%

Section: Sensor-to-segment Alignmentmentioning

confidence: 99%

Section: Processingmentioning

confidence: 99%

Section: Thresholdsmentioning

confidence: 99%

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Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

2019

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“…The development of wearable devices leads to implementation of ML algorithms directly on board [18,19], allowing for the reduction of the amount of data to be transmitted, and with consistent advantages in terms of power consumption and system usability [14]. To address the issues related to the need for platforms with good computing capacity, instead of general-purpose processors, dedicated hardware architectures such as field programmable gate arrays (FPGAs) can be selected for the implementation of the algorithms [20][21][22][23]. This allows for the control of the resources needed for the task and to optimize the system for performance or physical size, depending on the use case.…”

Section: Introductionmentioning

confidence: 99%

A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function

Bassoli

Bianchi

Munari

2020

Sensors

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Recent research in wearable sensors have led to the development of an advanced platform capable of embedding complex algorithms such as machine learning algorithms, which are known to usually be resource-demanding. To address the need for high computational power, one solution is to design custom hardware platforms dedicated to the specific application by exploiting, for example, Field Programmable Gate Array (FPGA). Recently, model-based techniques and automatic code generation have been introduced in FPGA design. In this paper, a new model-based floating-point accumulation circuit is presented. The architecture is based on the state-of-the-art delayed buffering algorithm. This circuit was conceived to be exploited in order to compute the kernel function of a support vector machine. The implementation of the proposed model was carried out in Simulink, and simulation results showed that it had better performance in terms of speed and occupied area when compared to other solutions. To better evaluate its figure, a practical case of a polynomial kernel function was considered. Simulink and VHDL post-implementation timing simulations and measurements on FPGA confirmed the good results of the stand-alone accumulator.

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Infusing a Convolutional Neural Network with Encoded Joint Node Image Data to Recognize 25 Daily Human Activities

Zhao,

Sun,

et al. 2023

Advanced Intelligent Systems

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Human activity recognition (HAR) has gained popularity in the field of computer vision such as video surveillance, security, and virtual reality. However, traditional methods are limited in terms of computations and holistic learning of human skeletal sequences. In this article, a new time‐series skeleton joint data imaging method is infused into an improved convolutional neural network to handle these problems. First, the raw time‐series data of 33 body nodes are transformed to red–green–blue images by encoding the 3D positional information to one pixel. Second, the LeNet‐5 network is enhanced by expanding the receptive field, introducing coordinate attention and the smooth maximum unit to improve smoothness and feature extraction. Third, the ability of coded images to express human activities is studied in various environments. It is shown in the experimental results that the method achieves an impressive accuracy of 98.02% in recognizing 25 daily human activities, such as running, writing, and walking. In addition, it is shown that the number of floating point operations, parameters, and inference time of the method are 0.08%, 0.47%, and 3.05%, respectively, of the average values for six other networks (including AlexNet, GoogLeNet, and MobileNet). The proposed method is thus a novel, lightweight, and high‐precision solution for HAR.

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A Wearable, Patient-Adaptive Freezing of Gait Detection System for Biofeedback Cueing in Parkinson's Disease

Cited by 43 publications

References 34 publications

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function

Infusing a Convolutional Neural Network with Encoded Joint Node Image Data to Recognize 25 Daily Human Activities

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