Using Wearable Inertial Sensors to Monitor Effectiveness of Different Types of Customized Orthoses during CrossFit® Training

Brognara, Lorenzo; Mazzotti, Antonio; Rossi, Federica; Lamia, Francesca; Artioli, Elena; Faldini, Cesare; Traina, Francesco

doi:10.3390/s23031636

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…Financial constraints and time expenditure have limited the use of these movement analysis laboratories in clinical practice; hence, the availability of cost-effective and reliable tools for gait analysis is paramount [ 35 ]. Gait analysis using smartphones and mobile apps is expected to play an increasingly important role in various clinical fields, where quantitative assessment of outcomes is crucial in achieving therapy goals [ 36 , 37 , 38 ]. Rehabilitation has significantly benefited from the development of smartphones and mobile apps [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Gait Assessment Using Smartphone Applications in Older Adults: A Scoping Review

Brognara

2024

Geriatrics

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Spatiotemporal parameters such as gait velocity and stride length are simple indicators of functional status and can be used to predict major adverse outcomes in older adults. A smartphone can be used for gait analysis by providing spatiotemporal parameters useful for improving the diagnosis and rehabilitation processes in frail people. The aim of this study was to review articles published in the last 20 years (from 2004 to 2024) concerning the application of smartphones to assess the spatiotemporal parameters of gait in older adults. This systematic review was performed in line with Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA), and original articles were identified by searching seven electronic databases: SciVerse (ScienceDirect), Excerpta Medica Database (EMBASE), Medline, Scopus, PubMed, Web of Science and the Cochrane Library. Studies were rigorously screened using the inclusion criteria of smartphones and mobile apps, older adults and spatiotemporal gait parameters, and results were narratively synthesized. Seventy-three articles were initially identified while searching the scientific literature regarding this topic. Eleven articles were selected and included in this review. Analysis of these studies covered information about gait assessment using mobile apps recorded in 723 older adults and 164 control cases. Analysis of data related to the application of smartphones to assess spatiotemporal parameters of gait in older adults showed moderate-to-excellent test–retest reliability and validity (ICCs around 0.9) of gait speed, the most common parameter reported. Additionally, gait speeds recorded with mobile apps showed excellent agreement when compared to gold standard systems. Smartphones and mobile apps are useful, non-invasive, low-cost and objective tools that are being extensively used to perform gait analysis in older adults. Smartphones and mobile apps can reliably identify spatiotemporal parameters related to adverse outcomes, such as a slow gait speed, as predictors and outcomes in clinical practice and research involving older adults.

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Section: Discussionmentioning

confidence: 99%

Gait Assessment Using Smartphone Applications in Older Adults: A Scoping Review

Brognara

2024

Geriatrics

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“…It has already been proven for a variety of different sports, such as running [37][38][39], ball sports [36,[40][41][42][43][44], winter sports [45,46], sports for the disabled [47], or fitness [48][49][50][51], that activity recognition algorithms are capable of detecting specific activities tied to these sports based on IMU data as input. Other studies that incorporated IMUs focus rather on the athletes' performance [52][53][54][55] or gait estimation [56,57] than activity recognition. Basketball has been studied by several works using IMU sensor data beginning in 2014.…”

Section: Related Work On Sports Studiesmentioning

confidence: 99%

Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors

Hoelzemann

Romero

Bock

et al. 2023

Sensors

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We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications of game analysis, guided training, and personal physical activity tracking. The dataset was recorded from two teams in separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both a repetitive basketball training session and a game. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset’s features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures.

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“…One of the key areas where motion capture technology has made significant contributions is in postural analysis and balance assessment. Postural control and balance are essential factors that influence an athlete's performance and susceptibility to injuries [8]. Traditional methods for assessing balance, such as force plates and laboratory-based motion capture systems, often face limitations in terms of cost, portability, and ecological validity.…”

Section: Introductionmentioning

confidence: 99%

Motion Capture Technology in Sports Scenarios: A Survey

Suo,

Tang,

2024

Sensors

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Motion capture technology plays a crucial role in optimizing athletes’ skills, techniques, and strategies by providing detailed feedback on motion data. This article presents a comprehensive survey aimed at guiding researchers in selecting the most suitable motion capture technology for sports science investigations. By comparing and analyzing the characters and applications of different motion capture technologies in sports scenarios, it is observed that cinematography motion capture technology remains the gold standard in biomechanical analysis and continues to dominate sports research applications. Wearable sensor-based motion capture technology has gained significant traction in specialized areas such as winter sports, owing to its reliable system performance. Computer vision-based motion capture technology has made significant advancements in recognition accuracy and system reliability, enabling its application in various sports scenarios, from single-person technique analysis to multi-person tactical analysis. Moreover, the emerging field of multimodal motion capture technology, which harmonizes data from various sources with the integration of artificial intelligence, has proven to be a robust research method for complex scenarios. A comprehensive review of the literature from the past 10 years underscores the increasing significance of motion capture technology in sports, with a notable shift from laboratory research to practical training applications on sports fields. Future developments in this field should prioritize research and technological advancements that cater to practical sports scenarios, addressing challenges such as occlusion, outdoor capture, and real-time feedback.

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Using Wearable Inertial Sensors to Monitor Effectiveness of Different Types of Customized Orthoses during CrossFit® Training

Cited by 4 publications

References 34 publications

Gait Assessment Using Smartphone Applications in Older Adults: A Scoping Review

Gait Assessment Using Smartphone Applications in Older Adults: A Scoping Review

Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors

Motion Capture Technology in Sports Scenarios: A Survey

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