GaitMotion: A Multitask Dataset for Pathological Gait Forecasting

• URL: http://arxiv.org/abs/2405.09569v1
• Date: Thu, 9 May 2024 14:45:02 GMT
• Title: GaitMotion: A Multitask Dataset for Pathological Gait Forecasting
• Authors: Wenwen Zhang, Hao Zhang, Zenan Jiang, Jing Wang, Amir Servati, Peyman Servati,
• Abstract summary: We introduce GaitMotion, a dataset leveraging wearable sensors to capture the patients' real-time movement with pathological gait. This dataset offers extensive ground-truth labeling for multiple tasks, including step/stride segmentation and step/stride length prediction. The wearable gait analysis suit captures the gait cycle, pattern, and parameters for both normal and pathological subjects.
• Score: 8.305371944195384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gait benchmark empowers uncounted encouraging research fields such as gait recognition, humanoid locomotion, etc. Despite the growing focus on gait analysis, the research community is hindered by the limitations of the currently available databases, which mostly consist of videos or images with limited labeling. In this paper, we introduce GaitMotion, a multitask dataset leveraging wearable sensors to capture the patients' real-time movement with pathological gait. This dataset offers extensive ground-truth labeling for multiple tasks, including step/stride segmentation and step/stride length prediction, empowers researchers with a more holistic understanding of gait disturbances linked to neurological impairments. The wearable gait analysis suit captures the gait cycle, pattern, and parameters for both normal and pathological subjects. This data may prove beneficial for healthcare products focused on patient progress monitoring and post-disease recovery, as well as for forensics technologies aimed at person reidentification, and biomechanics research to aid in the development of humanoid robotics. Moreover, the analysis has considered the drift in data distribution across individual subjects. This drift can be attributed to each participant's unique behavioral habits or potential displacement of the sensor. Stride length variance for normal, Parkinson's, and stroke patients are compared to recognize the pathological walking pattern. As the baseline and benchmark, we provide an error of 14.1, 13.3, and 12.2 centimeters of stride length prediction for normal, Parkinson's, and Stroke gaits separately. We also analyzed the gait characteristics for normal and pathological gaits in terms of the gait cycle and gait parameters.

Related papers

• Benchmarking Reliability of Deep Learning Models for Pathological Gait Classification [2.1548132286330453]
  Researchers have recently sought to leverage advances in machine learning algorithms to detect symptoms of altered gait. This paper analyzes existing approaches to identify gaps inhibiting translation. We propose our strong baseline called Asynchronous Multi-Stream Graph Convolutional Network (AMS-GCN) that can reliably differentiate multiple categories of pathological gaits.
  arXiv  Detail & Related papers  (2024-09-20T16:47:45Z)
• Explainable AI and Machine Learning Towards Human Gait Deterioration Analysis [0.0]
  We objectively analyze gait data and associate findings with clinically relevant biomarkers. We achieve classification accuracies of 98% F1 sc ores for each PhysioNet.org dataset and 95.5% F1 scores for the combined PhysioNet dataset.
  arXiv  Detail & Related papers  (2023-06-12T14:53:00Z)
• Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs [61.080598804629375]
  People with chronic pain unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities. We proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.
  arXiv  Detail & Related papers  (2022-12-20T12:56:28Z)
• Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data [67.60224656603823]
  Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time. Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles. Mobile passively sensed data captured from smartphones constitute an excellent alternative to profile patients' biorhythm.
  arXiv  Detail & Related papers  (2022-11-08T17:29:40Z)
• Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae [6.0431675579125415]
  Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among veterans and millions of Americans after traumatic exposures. Despite numerous studies conducted on APNS over the past decades, there has been limited progress in understanding the underlying neurobiological mechanisms.
  arXiv  Detail & Related papers  (2022-02-25T16:53:22Z)
• 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)
• Stroke recovery phenotyping through network trajectory approaches and graph neural networks [0.966840768820136]
  We analyze data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes.
  arXiv  Detail & Related papers  (2021-09-29T18:46:08Z)
• Classification of Pathological and Normal Gait: A Survey [0.0]
  Gait recognition is a term commonly referred to as an identification problem within the Computer Science field. This paper seeks to identify appropriate metrics, devices, and algorithms for collecting and analyzing data regarding patterns and modes of ambulatory movement across individuals.
  arXiv  Detail & Related papers  (2020-12-28T19:56:42Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• 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)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

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.