Determining Athlete's Injury with Wireless Body Area Sensor Network-Based Overhead Squat Testing

Chakraborty, Suryadip; Jamthe, Anagha; Ghosh, Sudip; Agrawal, Dharma P.

doi:10.1109/mass.2013.96

Cited by 4 publications

(5 citation statements)

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“…Of these, four assessed convergent validity [51][52][53][54]. Five studies pertained to known-groups validity [22, [55][56][57][58]. Two studies evaluated the longitudinal validity of a lower limb wearable sensor system in assessing joint ROM throughout a rehabilitation programme [60,61].…”

Section: Concurrent Validitymentioning

confidence: 99%

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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BackgroundAnalysis of lower limb exercises is traditionally completed with four distinct methods (i) 3D motion capture; (ii) depth-camera based systems (iii) visual analysis from a qualified exercise professional; (iv) self-assessment. Each method is associated with a number of limitations. ObjectiveThe aim of this systematic review is to synthesize and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises. Data SourcesA systematic review of PubMed, ScienceDirect and Scopus was conducted. Study Eligibility CriteriaArticles written in English and published in the last 10 years which contained an IMU system for the analysis of repetition-based targeted lower limb exercises were included. Study Appraisal and Synthesis MethodsThe quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised in to three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised. ResultsFrom the 2452 articles that were identified with the search strategies, 47 papers are included in this review. ConclusionsWearable inertial sensor systems for analysing lower limb exercises are a rapidly growing technology. Research over the past ten years has predominantly focused on validating measurements that the systems produce and classifying users' exercise quality. There have been very few user evaluation studies and no clinical trials in this field to date. Key PointsInertial measurement unit (IMU) systems have been extensively validated to successfully measure joint angle and temporal features during lower limb exercises.It is less understood if IMU systems can validly compute kinetic measures pertaining to lower limb exercises.IMU systems, which incorporate machine learning in to their data analysis pathways, have also been found to be effective in automated exercise detection and in classifying movement quality across a range of lower limb exercises.3

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Section: Concurrent Validitymentioning

confidence: 99%

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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“…Chakraborty et. al., [1] has provided a method to measure the foot pressure at different segments of foot. A design model was provided to evaluate the force based on the training routines and to determine the impact of postural balance on the athlete.…”

Section: Related Workmentioning

confidence: 99%

“…Wireless Body Area Network (WBAN) [1,2] is not only restricted to monitor the patients or to acquire the organ criticality status. Instead, it provides a proactive support to wider application areas.…”

Section: Introductionmentioning

confidence: 99%

An Organ Criticality and Session Preserved iM-Simple Protocol to Optimize WBAN for Different Application Areas

Sangwan¹

2019

IJAST

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The scope of WBAN is wide enough to monitor the health information, work performance, and perform activity classification of the individuals. Each of the operational and behavioral requirements has scope for different application areas. The mobility based iM-SIMPLE protocol already exists to identify the effective forwarder based on distance, energy and provides energy and packet size evaluation. In this paper, an improvement over the existing iM-SIMPLE protocol is provided by including the direction, organ criticality, and session preservation concepts. A more effective routing method is defined to observe each node and network level aspect to fulfill the communication requirements of each of the application areas considered in the present work. The algorithmic formulation is also provided to offer controlled communication in a single WBAN. The paper also provided the analytical study of each of the operational areas respective to the body sensor placement and effectiveness. The comparative results of the proposed improved iM-SIMPLE protocol are computed against the existing iM-SIMPLE protocol for six different application areas. The observations are taken against residual energy parameter. The node level evaluation verified that the proposed routing protocol has improved the node strength and network life for different application areas.

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“…Given its inconspicuous positioning, this system can be easily deployed on humans, ready to adapt to everyday living. [6] proposed a monitoring system for athletic monitoring, however a similar architecture can be applied here as well.…”

Section: Features From Gaitmentioning

confidence: 99%

“…The definition of LOOM states that: for every N feature vector set that are extracted, (N − 1) vector sets are applied for training the model. Here the N th vector (the last vector) obtained is being used to test the likelihood that it is a subset (or belongs to) that model (by applying equation 6). Given N possible iterations, with every iteration a distinct vector is applied for testing the model.…”

Section: Calculating Model Thresholdmentioning

confidence: 99%

The able amble

Meharia

Agrawal

2015

Proceedings of the 12th ACM International Conference on Computing Frontiers

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With the advent of wearable devices and commonality of on-body monitoring devices, it is anticipated that a day will come in the future where body-area networks will become commonplace in our lives. It is envisioned that the whole process will be automated wherein a user wearing such a device automatically enables the security mechanism and establishes communication between that user and his/her surroundings. This paper addresses a technique to identify the wearer of the device by way of Gaussian Mixture Models (GMM), allowing for identification and verification before establishing communication. It suggests using gait as a metric for identity association using wearable sensors.

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Determining Athlete's Injury with Wireless Body Area Sensor Network-Based Overhead Squat Testing

Cited by 4 publications

References 1 publication

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

An Organ Criticality and Session Preserved iM-Simple Protocol to Optimize WBAN for Different Application Areas

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