Step length measurement in the wild using FMCW radar

• URL: http://arxiv.org/abs/2401.01868v1
• Date: Wed, 3 Jan 2024 18:23:30 GMT
• Title: Step length measurement in the wild using FMCW radar
• Authors: Parthipan Siva, Alexander Wong, Patricia Hewston, George Ioannidis, Dr. Jonathan Adachi, Dr. Alexander Rabinovich, Andrea Lee, Alexandra Papaioannou
• Abstract summary: Radar-based step length measurement system for the home is proposed. method was evaluated in a clinical environment, involving 35 frail older adults, to establish its validity.
• Score: 81.9433966586583
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With an aging population, numerous assistive and monitoring technologies are under development to enable older adults to age in place. To facilitate aging in place predicting risk factors such as falls, and hospitalization and providing early interventions are important. Much of the work on ambient monitoring for risk prediction has centered on gait speed analysis, utilizing privacy-preserving sensors like radar. Despite compelling evidence that monitoring step length, in addition to gait speed, is crucial for predicting risk, radar-based methods have not explored step length measurement in the home. Furthermore, laboratory experiments on step length measurement using radars are limited to proof of concept studies with few healthy subjects. To address this gap, a radar-based step length measurement system for the home is proposed based on detection and tracking using radar point cloud, followed by Doppler speed profiling of the torso to obtain step lengths in the home. The proposed method was evaluated in a clinical environment, involving 35 frail older adults, to establish its validity. Additionally, the method was assessed in people's homes, with 21 frail older adults who had participated in the clinical assessment. The proposed radar-based step length measurement method was compared to the gold standard Zeno Walkway Gait Analysis System, revealing a 4.5cm/8.3% error in a clinical setting. Furthermore, it exhibited excellent reliability (ICC(2,k)=0.91, 95% CI 0.82 to 0.96) in uncontrolled home settings. The method also proved accurate in uncontrolled home settings, as indicated by a strong agreement (ICC(3,k)=0.81 (95% CI 0.53 to 0.92)) between home measurements and in-clinic assessments.

Related papers

• Depth-Weighted Detection of Behaviours of Risk in People with Dementia using Cameras [3.6855408155998215]
  The behavioural and psychological symptoms of dementia, such as agitation and aggression, present a significant health and safety risk in residential care settings. Care facilities have video cameras in place for digital monitoring of public spaces, which can be leveraged to develop an automated behaviours of risk detection system. One of the challenges in our previous study was the presence of false alarms due to obstruction of view by activities happening close to the camera. We proposed a novel depth-weighted loss function to train a customized convolutional autoencoder to enforce equivalent importance to the events happening both near and far from the cameras.
  arXiv  Detail & Related papers  (2024-08-28T04:12:07Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• Towards Clinical Prediction with Transparency: An Explainable AI Approach to Survival Modelling in Residential Aged Care [0.0]
  The study applies machine learning to create a survival model for aged care, aligning with clinical insights on mortality risk factors. Key mortality predictors include age, male gender, mobility, health status, pressure ulcer risk, and appetite.
  arXiv  Detail & Related papers  (2023-12-01T01:11:16Z)
• Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach [4.826264675070211]
  This paper proposes a novel calibration method for wearable accelerometers. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction.
  arXiv  Detail & Related papers  (2023-07-10T19:25:45Z)
• In-vehicle alertness monitoring for older adults [63.359033532099204]
  We present a system for in-vehicle alertness monitoring for older adults. We implemented a prototype traveler monitoring system and evaluated the alertness detection algorithm on ten older adults (70 years and older. This study is the first of its kind for a hitherto under-studied population and has implications for future work on algorithm development and system design through participatory methods.
  arXiv  Detail & Related papers  (2022-08-17T06:07:37Z)
• Evaluation of Non-Invasive Thermal Imaging for detection of Viability of Onchocerciasis worms [0.0]
  Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive procedure. This paper proposes use of machine learning over thermal imaging to non-invasively and accurately predict the viability of worms.
  arXiv  Detail & Related papers  (2022-03-23T10:06:08Z)
• Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data [52.40058724040671]
  Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
  arXiv  Detail & Related papers  (2021-10-19T11:45:01Z)
• C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak [54.39837683016444]
  C-Watcher aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks.
  arXiv  Detail & Related papers  (2020-12-22T17:02:54Z)
• Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning [2.231365407061881]
  We propose a supervised deep learning method for the direct estimation of aortic diameters. Our approach makes use of a 3D+2D convolutional neural network (CNN) that takes as input a 3D scan and outputs the aortic diameter at a given location. Overall, the 3D+2D CNN achieved a mean absolute error between 2.2-2.4 mm depending on the considered aortic location.
  arXiv  Detail & Related papers  (2020-09-09T18:28:00Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

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.