A Multisensor Integration-Based Complementary Tool for Monitoring Recovery Progress of Anterior Cruciate Ligament-Reconstructed Subjects

Malik, Owais Ahmed; Senanayake, S. M. N. Arosha; Zaheer, Danish

doi:10.1109/tmech.2014.2376199

Cited by 10 publications

(14 citation statements)

References 34 publications

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“…(1) vastus lateralis, (2) vastus medialis, (3) biceps femoris and (4) semitendinosus. The changes in these muscles have been reported in previous studies after knee injuries [3]. The EMG data were recorded at a sampling rate of 960Hz with 12 bit Analog/Digital conversion.…”

Section: A Data Acquisition and Pre-processingmentioning

confidence: 89%

“…Further, the RGB image based gait classification system has also been proposed previously [8]. In contrast to optical/camera systems, wearable wireless MEMS have also gained a lot of attention for human activity analysis due to their small size and easy setup in any kind of environment [3]. Multiple sensors' integration in wearable systems provide different signals at the same time which makes such a low-cost system very suitable for gait monitoring [5,9].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Further, since the lower limb injuries cause muscle atrophy and neuromuscular disorders so the electromyography signals have also been evaluated to monitor the changes in the gait patterns in few studies [14]. However, most of the existing studies focus on individual kinematics or sptiotemporal or electromyography signals and there exists only few studies which have integrated these biosignals to analyze the gait patterns in subjects [3,5,15].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Human gait analysis has various applications in the fields of surveillance, biometrics and medicine [1][2][3]. For security applications, the purpose of gait analysis is to differentiate among individuals or identify a person based on his/her walking style.…”

Section: Introductionmentioning

confidence: 99%

“…However, in the absence of proper monitoring and rehabilitation, long term problems can be noticed in the subjects having these injuries or surgeries. Dynamic joint instability, neuromuscular impairments, cartilage degeneration, early onset of osteoarthritis and progressive arthritic changes have been observed in subjects having knee injuries and surgeries [3,6].…”

Section: Introductionmentioning

confidence: 99%

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Deep Autoencoder for Identification of Abnormal Gait Patterns Based on Multimodal Biosignals

Malik¹

2021

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Section: A Data Acquisition and Pre-processingmentioning

confidence: 89%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

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Section: Introductionmentioning

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A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation

et al. 2023

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Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to most people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes. Although many portable sensing approaches have demonstrated promising results during various assessments related to ACL injury, they have not yet been widely adopted as tools for out-of-lab assessment. The purpose of this review is to summarize research on out-of-lab portable sensing applied to ACL and ACLR and offer our perspectives on new opportunities for future research and development. We identified 49 original research articles on out-of-lab ACL-related assessment; the most common sensing modalities were inertial measurement units, depth cameras, and RGB cameras. The studies combined portable sensors with direct feature extraction, physics-based modeling, or machine learning to estimate a range of biomechanical parameters (e.g., knee kinematics and kinetics) during jump-landing tasks, cutting, squats, and gait. Many of the reviewed studies depict proof-of-concept methods for potential future clinical applications including ACL injury risk screening, injury prevention training, and rehabilitation assessment. By synthesizing these results, we describe important opportunities that exist for clinical validation of existing approaches, using sophisticated modeling techniques, standardization of data collection, and creation of large benchmark datasets. If successful, these advances will enable widespread use of portable-sensing approaches to identify ACL injury risk factors, mitigate high-risk movements prior to injury, and optimize rehabilitation paradigms.

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A Multisensor Integration-Based Complementary Tool for Monitoring Recovery Progress of Anterior Cruciate Ligament-Reconstructed Subjects

Cited by 10 publications

References 34 publications

Deep Autoencoder for Identification of Abnormal Gait Patterns Based on Multimodal Biosignals

Deep Autoencoder for Identification of Abnormal Gait Patterns Based on Multimodal Biosignals

RETRACTED ARTICLE: An effective stadium monitoring control algorithm based on big data in emerging mobile networks

A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation

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