Assessment methods of post-stroke gait: A scoping review of technology-driven approaches to gait characterization and analysis
DM Mohan, AH Khandoker, SA Wasti… - Frontiers in …, 2021 - frontiersin.org
Background: Gait dysfunction or impairment is considered one of the most common and
devastating physiological consequences of stroke, and achieving optimal gait is a key goal …
devastating physiological consequences of stroke, and achieving optimal gait is a key goal …
Wearable movement sensors for rehabilitation: a focused review of technological and clinical advances
Recent technologic advancements have enabled the creation of portable, low-cost, and
unobtrusive sensors with tremendous potential to alter the clinical practice of rehabilitation …
unobtrusive sensors with tremendous potential to alter the clinical practice of rehabilitation …
Multimodal assessment of Parkinson's disease: a deep learning approach
JC Vásquez-Correa, T Arias-Vergara… - IEEE journal of …, 2018 - ieeexplore.ieee.org
Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor
symptoms. Particularly, difficulties to start/stop movements have been observed in patients …
symptoms. Particularly, difficulties to start/stop movements have been observed in patients …
Internet of things technologies and machine learning methods for Parkinson's disease diagnosis, monitoring and management: a systematic review
KM Giannakopoulou, I Roussaki, K Demestichas - Sensors, 2022 - mdpi.com
Parkinson's disease is a chronic neurodegenerative disease that affects a large portion of
the population, especially the elderly. It manifests with motor, cognitive and other types of …
the population, especially the elderly. It manifests with motor, cognitive and other types of …
Analysis of the performance of 17 algorithms from a systematic review: Influence of sensor position, analysed variable and computational approach in gait timing …
Background The quantification of gait temporal parameters (ie step time, stance time) is
crucial in human motion analysis and requires the accurate identification of gait events (ie …
crucial in human motion analysis and requires the accurate identification of gait events (ie …
[HTML][HTML] Video-based pose estimation for gait analysis in stroke survivors during clinical assessments: a proof-of-concept study
Recent advancements in deep learning have produced significant progress in markerless
human pose estimation, making it possible to estimate human kinematics from single …
human pose estimation, making it possible to estimate human kinematics from single …
Data-driven based approach to aid Parkinson's disease diagnosis
This article presents a machine learning methodology for diagnosing Parkinson's disease
(PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the …
(PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the …
[HTML][HTML] Real-life gait performance as a digital biomarker for motor fluctuations: the Parkinson@ Home validation study
Background Wearable sensors have been used successfully to characterize bradykinetic
gait in patients with Parkinson disease (PD), but most studies to date have been conducted …
gait in patients with Parkinson disease (PD), but most studies to date have been conducted …
Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson's Disease patients
Background Identification of individual gait events is essential for clinical gait analysis,
because it can be used for diagnostic purposes or tracking disease progression in …
because it can be used for diagnostic purposes or tracking disease progression in …
Deep learning in gait parameter prediction for OA and TKA patients wearing IMU sensors
Quantitative assessments of patient movement quality in osteoarthritis (OA), specifically
spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns …
spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns …