Edge Deep Learning Enabled Freezing of Gait Detection in Parkinson's Patients

• URL: http://arxiv.org/abs/2212.00729v1
• Date: Sun, 27 Nov 2022 17:05:39 GMT
• Title: Edge Deep Learning Enabled Freezing of Gait Detection in Parkinson's Patients
• Authors: Ourong Lin, Tian Yu, Yuhan Hou, Yi Zhu, and Xilin Liu
• Abstract summary: This paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease. Three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at ankle, thigh, and truck. Each sensor node can independently detect FoG using an on-device deep learning (DL) model, featuring a squeeze and excitation convolutional neural network (CNN)
• Score: 7.612338614344926
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease. Three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at ankle, thigh, and truck. Each sensor node can independently detect FoG using an on-device deep learning (DL) model, featuring a squeeze and excitation convolutional neural network (CNN). In a validation using a public dataset, the prototype developed achieved a FoG detection sensitivity of 88.8% and an F1 score of 85.34%, using less than 20 k trainable parameters per sensor node. Once FoG is detected, an auditory signal will be generated to alert users, and the alarm signal will also be sent to mobile phones for further actions if needed. The sensor node can be easily recharged wirelessly by inductive coupling. The system is self-contained and processes all user data locally without streaming data to external devices or the cloud, thus eliminating the cybersecurity risks and power penalty associated with wireless data transmission. The developed methodology can be used in a wide range of applications.

Related papers

• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• DensePose From WiFi [86.61881052177228]
  We develop a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. Our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches.
  arXiv  Detail & Related papers  (2022-12-31T16:48:43Z)
• Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks [3.509171590450989]
  NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users. A network of wireless sensors is needed to monitor the spectrum for signal transmissions of interest over a large deployment area. To improve the accuracy, individual sensors may exchange sensing data or sensor results with each other or with a fusion center.
  arXiv  Detail & Related papers  (2022-04-06T18:18:42Z)
• MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures [4.92674421365689]
  We propose MDPose, a novel framework for human skeletal motion reconstruction based on WiFi micro-Doppler signatures. It provides an effective solution to track human activities by reconstructing a skeleton model with 17 key points. MDPose outperforms state-of-the-art RF-based pose estimation systems.
  arXiv  Detail & Related papers  (2022-01-11T21:46:28Z)
• Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough [55.45063681652457]
  We propose a machine learning method to quickly triage COVID-19 using recordings made on consumer devices. The approach combines signal processing methods with fine-tuned deep learning networks and provides methods for signal denoising, cough detection and classification. We have also developed and deployed a mobile application that uses symptoms checker together with voice, breath and cough signals to detect COVID-19 infection.
  arXiv  Detail & Related papers  (2021-07-12T08:07:56Z)
• Radar Voxel Fusion for 3D Object Detection [0.0]
  This paper develops a low-level sensor fusion network for 3D object detection. The radar sensor fusion proves especially beneficial in inclement conditions such as rain and night scenes.
  arXiv  Detail & Related papers  (2021-06-26T20:34:12Z)
• A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC [0.40690419770123604]
  A neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression. This is the first radiation tolerant on-detector ASIC implementation of a neural network that has been designed for particle physics applications.
  arXiv  Detail & Related papers  (2021-05-04T18:06:23Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications [71.1911136637719]
  We show how provable guarantees can be naturally applied to other real world settings. We show how specific intervals of fuel injection quantities can be targeted to maximize robustness for certain ranges.
  arXiv  Detail & Related papers  (2020-06-30T23:33:17Z)
• Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits [3.2654923574107357]
  Falls have serious consequences and are prevalent in acute hospitals and nursing homes caring for older people. Technological interventions to mitigate the risk of falling aim to automatically monitor bed-exit events and alert healthcare personnel to provide timely supervisions. We observe that frequency-domain information related to patient activities exist predominantly in very low frequencies. We investigate a batteryless sensing modality with low cost wirelessly powered Radio Frequency Identification (RFID) technology with the potential for convenient integration into clothing, such as hospital gowns.
  arXiv  Detail & Related papers  (2020-03-19T00:58:30Z)
• OmniTact: A Multi-Directional High Resolution Touch Sensor [109.28703530853542]
  Existing tactile sensors are either flat, have small sensitive fields or only provide low-resolution signals. We introduce OmniTact, a multi-directional high-resolution tactile sensor. We evaluate the capabilities of OmniTact on a challenging robotic control task.
  arXiv  Detail & Related papers  (2020-03-16T01:31:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.