Detection and assessment of Parkinson's disease based on gait analysis: A survey

Guo, Yao; Yang, Jun; Liu, Yuxuan; Chen, Xun; Yang, Guang‐Zhong

doi:10.3389/fnagi.2022.916971

Cited by 26 publications

(21 citation statements)

References 212 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…20 Other studies monitoring gait parameters in PD used wearable sensors or wearable devices based on different types of sensors. [31][32][33][34][35][36][37][38][39] Figure 1 Flow diagram of literature search strategy.…”

Section: Resultsmentioning

confidence: 99%

“…20 Other studies monitoring gait parameters in PD used wearable sensors or wearable devices based on different types of sensors. 31 32 33 34 35 36 37 38 39…”

Section: Resultsmentioning

confidence: 99%

“…The advancement of sensor technology, motion capture systems, and force platforms have been employed as more objective and precise methods for evaluating PD patient's gait. 33 Sensor and Wearable devices (SWD) facilitate and monitor an individual's activity. They are an important asset for intervention and monitoring and are easy to integrate into the patients' daily routines.…”

Section: Wearable Sensorsmentioning

confidence: 99%

See 2 more Smart Citations

Assisted technology in Parkinson's disease gait: what's up?

Capato,

Chen,

Miranda

et al. 2024

Arq Neuropsiquiatr

View full text Add to dashboard Cite

Background Gait disturbances are prevalent and debilitating symptoms, diminishing mobility and quality of life for Parkinson's disease (PD) individuals. While traditional treatments offer partial relief, there is a growing interest in alternative interventions to address this challenge. Recently, a remarkable surge in assisted technology (AT) development was witnessed to aid individuals with PD. Objective To explore the burgeoning landscape of AT interventions tailored to alleviate PD-related gait impairments and describe current research related to such aim. Methods In this review, we searched on PubMed for papers published in English (2018-2023). Additionally, the abstract of each study was read to ensure inclusion. Four researchers searched independently, including studies according to our inclusion and exclusion criteria. Results We included studies that met all inclusion criteria. We identified key trends in assistive technology of gait parameters analysis in PD. These encompass wearable sensors, gait analysis, real-time feedback and cueing techniques, virtual reality, and robotics. Conclusion This review provides a resource for guiding future research, informing clinical decisions, and fostering collaboration among researchers, clinicians, and policymakers. By delineating this rapidly evolving field's contours, it aims to inspire further innovation, ultimately improving the lives of PD patients through more effective and personalized interventions.

show abstract

Section: Resultsmentioning

confidence: 99%

“…20 Other studies monitoring gait parameters in PD used wearable sensors or wearable devices based on different types of sensors. 31 32 33 34 35 36 37 38 39…”

Section: Resultsmentioning

confidence: 99%

Section: Wearable Sensorsmentioning

confidence: 99%

See 1 more Smart Citation

Assisted technology in Parkinson's disease gait: what's up?

Capato,

Chen,

Miranda

et al. 2024

Arq Neuropsiquiatr

View full text Add to dashboard Cite

show abstract

“…Gait recognition is a technique that evaluates an individual's health status and rehabilitation level by analyzing their walking patterns [1][2][3] . In the medical and rehabilitation fields, gait recognition is of great significance for evaluating the rehabilitation process of patients with neurological diseases, musculoskeletal diseases, and neurological injuries [4][5][6] . In addition, gait recognition can also be applied in fields such as robot navigation, animation production, and movie special effects [7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

EEG signal motion recognition based on GSA-SVM

Liu,

Chang,

Zhou

et al. 2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

View full text Add to dashboard Cite

This paper reviews the research progress of gait recognition and rehabilitation training control based on EEG signals. Firstly, the importance and application scenarios of gait recognition research were introduced, with a focus on discussing gait recognition methods based on EEG signals. Then, the basic characteristics of EEG signals and the main EEG signal analysis methods used for gait recognition were summarized, including time-domain analysis, frequency-domain analysis, and time-frequency analysis. Secondly, this article collected some motion EEG signals and selected corresponding recognition algorithms through data processing, feature extraction, and classifier selection to perform gait recognition on the EEG signals. The results show that the algorithm proposed in this paper has a high recognition rate.

show abstract

“…Through instrumentation, a plethora of gait parameters has been identified (e.g., spatiotemporal, kinematic, kinetic, etc.) [4,5,13] enabling the quantification of human gait of a diverse set of populations (e.g., healthy or patient individuals, etc.) [4,14].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data

Tsakanikas

Ntanis

Rigas

et al. 2023

Sensors

View full text Add to dashboard Cite

Parkinson’s disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients’ mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.

show abstract

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Cited by 26 publications

References 212 publications

Assisted technology in Parkinson's disease gait: what's up?

Assisted technology in Parkinson's disease gait: what's up?

EEG signal motion recognition based on GSA-SVM

Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data

Contact Info

Product

Resources

About