Deep Learning in Gait Analysis for Security and Healthcare

Costilla-Reyes, Omar; Vera-Rodríguez, Rubén; Alharthi, Abdullah; Yunas, Syed U.; Ozanyan, Krikor B.

doi:10.1007/978-3-030-31760-7_10

Cited by 16 publications

(13 citation statements)

References 44 publications

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“…However, these studies are specific to the security applications of gait analysis and are not purely based on wearable sensors and ML. The papers [49], [50] are specific to deep learning approaches for security and healthcare using gait analysis. Similarly, a survey on gait analysis limited to fall detection and fall prevention is presented in [51].…”

Section: Introductionmentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

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Section: Introductionmentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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“…Moreover, it eliminates the need for manual feature engineering, reducing possible human biases and removing the need for advanced expertise (Zhang et al, 2020). DL is capable of learning data representation in an unprocessed or raw form, and its high performance and expressive power in one specific domain can be transferred to other contexts, providing a flexible adaptation to problems (Bengio, 2009;Lecun et al, 2015;Miotto et al, 2017;Vieira et al, 2017;Chauhan et al, 2019;Esteva et al, 2019;Costilla-Reyes et al, 2020;Zhang et al, 2020). Despite all the advantages, it is crucial to consider that DL techniques require very large datasets to perform, which may be too hard to achieve, expensive, or time-consuming to obtain; thus, ML may be more feasible and efficient (Zhang et al, 2020).…”

Section: A New Integrated Approach To MCI Assessmentmentioning

confidence: 99%

“…A gait cycle is defined by ongoing changes in the sequential configurations of the joints allowed by muscle activation, which is controlled by neural mechanisms depending on the integrity of somatosensory, motor, and cognitive integration cerebral networks ( Perry and Burnfield, 2010 ; Caldas et al, 2017 ; Costilla-Reyes et al, 2020 ). Successful locomotion is indeed a dual task requiring the ability to simultaneously perform a cognitive task that could interfere with gait performance, particularly in elderly people ( Pedroli et al, 2018 ; Costilla-Reyes et al, 2020 ). A decrease in attentional and executive functioning is physiological in aging and could impact this simultaneous execution ( Hsu et al, 2012 ; Montero-Odasso et al, 2012 ; Wang et al, 2015 ; Gwak et al, 2018 ; Pedroli et al, 2018 ).…”

Section: A New Integrated Approach To MCI Assessmentmentioning

confidence: 99%

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

Cavedoni

Chirico

Pedroli

et al. 2020

Front. Hum. Neurosci.

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Elderly people affected by Mild Cognitive Impairment (MCI) usually report a perceived decline in cognitive functions that deeply impacts their quality of life. This subtle waning, although it cannot be diagnosable as dementia, is noted by caregivers on the basis of their relative's behaviors. Crucially, if this condition is also not detected in time by clinicians, it can easily turn into dementia. Thus, early detection of MCI is strongly needed. Classical neuropsychological measures-underlying a categorical model of diagnosis-could be integrated with a dimensional assessment approach involving Virtual Reality (VR) and Artificial Intelligence (AI). VR can be used to create highly ecologically controlled simulations resembling the daily life contexts in which patients' daily instrumental activities (IADL) usually take place. Clinicians can record patients' kinematics, particularly gait, while performing IADL (Digital Biomarkers). Then, Artificial Intelligence employs Machine Learning (ML) to analyze them in combination with clinical and neuropsychological data. This integrated computational approach would enable the creation of a predictive model to identify specific patterns of cognitive and motor impairment in MCI. Therefore, this new dimensional cognitive-behavioral assessment would reveal elderly people's neural alterations and impaired cognitive functions, typical of MCI and dementia, even in early stages for more time-sensitive interventions.

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“…Gait analysis [15] utilizes the unique patterns of individuals' walking styles as distinctive identifiers. This noninvasive method of biometric processing has found significant applications across several domains, including security and surveillance [9] and healthcare [12]. Gait patterns offer insights into the identity of a person [9], demographics [4], emotions [24] and mental state [10].…”

Section: Introductionmentioning

confidence: 99%

Learning Gait Representations with Noisy Multi-Task Learning

Cosma

Radoi

2022

Sensors

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Gait analysis is proven to be a reliable way to perform person identification without relying on subject cooperation. Walking is a biometric that does not significantly change in short periods of time and can be regarded as unique to each person. So far, the study of gait analysis focused mostly on identification and demographics estimation, without considering many of the pedestrian attributes that appearance-based methods rely on. In this work, alongside gait-based person identification, we explore pedestrian attribute identification solely from movement patterns. We propose DenseGait, the largest dataset for pretraining gait analysis systems containing 217K anonymized tracklets, annotated automatically with 42 appearance attributes. DenseGait is constructed by automatically processing video streams and offers the full array of gait covariates present in the real world. We make the dataset available to the research community. Additionally, we propose GaitFormer, a transformer-based model that after pretraining in a multi-task fashion on DenseGait, achieves 92.5% accuracy on CASIA-B and 85.33% on FVG, without utilizing any manually annotated data. This corresponds to a +14.2% and +9.67% accuracy increase compared to similar methods. Moreover, GaitFormer is able to accurately identify gender information and a multitude of appearance attributes utilizing only movement patterns. The code to reproduce the experiments is made publicly.

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Deep Learning in Gait Analysis for Security and Healthcare

Cited by 16 publications

References 44 publications

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

Learning Gait Representations with Noisy Multi-Task Learning

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