Handcrafted Features for Human Gait Recognition: CASIA-A Dataset

Rani, Veenu; Kumar, Munish; Singh, Bhupinder

doi:10.1007/978-3-031-21385-4_7

Cited by 6 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These set of techniques either compute several statistical measurements on input data (e.g., average, variance, skewness) or extract more complex gait characteristics, which may include stride length, joint angles, and other related features. In the context of machine learning, handcrafted features refer to manually designed features that are extracted from raw data and used as input to a classifier [ 8 , 28 , 29 , 30 ]. For instance, the technique proposed in [ 31 ] extracted several statistical quantities on input data (e.g., mean, median, mode, standard deviation, skewness, and kurtosis) to show the gait fluctuation in a patient with Parkinson’s.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Since the gait comprises continuous time series data, we need to extract a set of relevant features from these raw data to represent gait patterns. There are several approaches in the literature to extract features, and they can be categorized into three groups: (1) handcrafted [ 8 ], (2) codebook-based [ 9 ], and (3) automatic deep learning-based [ 10 ] feature extraction approaches. The handcrafted features are usually a few statistical quantities, e.g., mean, variance, skewness, kurtosis, etc., that are extracted from raw data based on the prior knowledge of experts in the application domain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

Fatima,

Khan,

Nisar

et al. 2023

Sensors

View full text Add to dashboard Cite

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

Fatima,

Khan,

Nisar

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Quasi gait recognition is very effective for gait recognition because it is less sensitive to these factors. Independent component analysis ICA (Lu and Zhang, 2007) and modified ICA (Rani and Arumugam, 2010) are also very effective for gait recognition. ICA is similar like Principal component analysis PCA where the difference is that ICA has statistically independent components.…”

Section: Introductionmentioning

confidence: 99%

Survey of Human Gait Analysis and Recognition for Medical and Forensic Applications

Jana

Das

Basu

et al. 2021

International Journal of Digital Crime and Forensics

View full text Add to dashboard Cite

Gait is a behavioural biometric which sometimes changes due to diseases but it is still a strong identification metric that is widely used in forensic works, state biometric preserve sectors, and medical laboratories. Gait analysis sometimes helps to identify person’s present mental state which reflects on physiological therapy for improved biological system. There are various gait measurement forms which expand the research area from crime detection to medical enhancement. Many research works have been done so far for gait recognition. Many researchers focused on skeleton image of people to extract gait features and many worked on stride length. Various sensors have been used to detect gait in various light forms. This paper is a brief survey of works on gait recognition, collected from various sources of science and technology literature. We have discussed few efficient models that worked best as well as we have discussed about few data sets available.

show abstract

Human gait recognition: A systematic review

Rani

Kumar

2023

Multimed Tools Appl

View full text Add to dashboard Cite

Handcrafted Features for Human Gait Recognition: CASIA-A Dataset

Cited by 6 publications

References 27 publications

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

Survey of Human Gait Analysis and Recognition for Medical and Forensic Applications

Human gait recognition: A systematic review

Contact Info

Product

Resources

About