Browsing by Author "Priyanka Bogia"

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Leveraging artificial intelligence and machine learning in sport sciences: a systematic literature review of applications, outcomes, and future directions
(Springer-Verlag Italia s.r.l., 2025) Ravinder Kumar; Priyanka Bogia; Ameen Ul Haq; Vikram Singh; T. Onima Reddy
Background: Artificial intelligence (AI) and machine learning (ML) are transforming sports sciences by enabling precise performance analysis, injury prevention, and rehabilitation. However, inconsistencies in protocols, lack of standardization, and practical deployment challenges limit their real-world impact. Methodology: This PRISMA-guided systematic review was registered with PROSPERO (CRD42021235527). Literature searches were conducted across Scopus, PubMed, and Web of Science for studies published between 2018 and 2024. The PICO framework guided selection, and methodological quality was assessed using AMSTAR-2. Results: A total of 40 studies were included. CNNs (n = 18; accuracy = 96 ± 1.5%) and LSTMs (n = 10; 92 ± 2%) performed best for motion tracking and gait analysis. SVMs (n = 17; 81 ± 3%) and RFs (n = 16; 80 ± 2.8%) were widely used in EMG, GPS, and workload analysis. Real-time feedback (n = 18) showed latency below 100 ms but was mainly validated in controlled settings; only 25% tested systems outdoors. Wearable sensors (e.g., IMUs, gyroscopes) were used in 34 studies, most frequently on the lower limbs. Participants averaged 42 ± 28 in number, with a bias toward young, healthy males. Key limitations included algorithm opacity, small homogeneous samples, inconsistent sensor setups, and limited interpretability or clinical integration. Conclusion: AI/ML tools show high accuracy and responsiveness for biomechanical monitoring, injury risk detection, and training adaptation. However, scalability remains hindered by technical, demographic, and practical barriers. Future studies must prioritize diverse populations, standardize sensor and data protocols, emphasize model explainability, and ensure deployment in real-world sports environments to close the gap between algorithmic performance and applied utility. Trial registration: CRD42021235527 © The Author(s), under exclusive licence to Springer-Verlag Italia S.r.l., part of Springer Nature 2025.
The running gait analysis technology: A comprehensive systematic literature review
(Reed Elsevier India Pvt. Ltd., 2025) Ravinder Kumar; Priyanka Bogia; Vikram Singh; T. Onima Reddy
Background: Running is practiced worldwide, but more than 50 % of runners suffer some form of musculoskeletal injury each year. Biomechanics of running is an important aspect of sports medicine and gait analysis is central in the study of running mechanics for prevention of injuries and enhancing performance. Objectives: The purpose of this systematic literature review is to Saragiotto et al. (2014 Apr 4) 1 assess the methods employed in conducting gait analysis studies from 2020 to 2024, 2 discuss spatiotemporal characteristics, bilateral asymmetry, and RRI, (Lenhart et al., 2014 Mar) 3 present wearable technology, and (Willson et al., 2014) 4 provide recommendations for future research and application based on the findings. Methods: The study was conducted following the PRISMA guidelines and was registered in the PROSPERO database under the number CRD42024572642. The systematic search of articles was performed in the Scopus database, considering the articles written in English and published in journals between 2004 and 2024, which are focused on the analysis of running gait. Data were collected, pre-processed, and processed according to certain inclusion and exclusion criteria. Results: Of 2175 articles, only 43 studies were included. The studies were mainly concerned with spatiotemporal features (Patino and Ferreira, 2018), 16 gait asymmetry and injuries (Crowther et al., 2007 Jun 1), 9 biomechanics (Mason et al., 2023 Sep 1), 8 and gait measurement tools (Schubert et al., 2014 May 1).10 IMUs, accelerometers, and pressure sensors were established as wearable technologies that can be used to monitor gait in the sports setting. Conclusion: In this review, we discuss the latest developments in wearable technology for gait analysis, which can be considered a viable alternative to laboratory-based methods. However, the need to use standard methods and validation procedures has not lost its importance as it is crucial for the practical application of these technologies. Protocol: Registration number CRD42024572642. © 2024 Professor P K Surendran Memorial Education Foundation