Wearable Technology and Movement Analysis in Athletic Performance and Rehabilitation

A special issue of Journal of Functional Morphology and Kinesiology (ISSN 2411-5142). This special issue belongs to the section "Kinesiology and Biomechanics".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 3543

Editor

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the application of wearable sensors and motion analysis tools as a means of deepening our understanding of human movement, optimizing physical performance, and enhancing rehabilitation outcomes. This Issue aims to showcase how movement analysis and performance monitoring are being applied to real-world challenges in sports and clinical rehabilitation.

We welcome contributions that use motion analysis to evaluate motor function, track recovery progress, prevent injury, and personalize rehabilitation or training programs. Topics of interest include biomechanical assessments, motor control strategies, outcome-based rehabilitation protocols, and performance optimization through movement-focused interventions.

Submissions from kinesiology, physiotherapy, sports science, and related disciplines are encouraged. Both original research and systematic reviews that demonstrate the impact of movement-focused assessment and monitoring are invited.

Dr. Juri Taborri
Guest Editor

Manuscript Submission Information

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Keywords

  • wearable sensors
  • motion analysis
  • sports performance
  • rehabilitation technology
  • biomechanics
  • injury prevention
  • remote monitoring
  • physiotherapy
  • human movement
  • kinesiology

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Published Papers (3 papers)

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Research

17 pages, 1491 KB  
Article
Functional Differences Across Playing Roles in Volleyball: A Sensor-Based Assessment
by Juri Taborri, Mauro Strippoli, Luca Molinaro and Stefano Rossi
J. Funct. Morphol. Kinesiol. 2026, 11(2), 238; https://doi.org/10.3390/jfmk11020238 - 13 Jun 2026
Viewed by 392
Abstract
Objectives: Volleyball playing positions are associated with different functional demands. This study compared postural control, jump performance, and upper-limb mobility across playing roles in competitive male volleyball players. Methods: Fifty male volleyball players competing in the Italian Serie C championship were [...] Read more.
Objectives: Volleyball playing positions are associated with different functional demands. This study compared postural control, jump performance, and upper-limb mobility across playing roles in competitive male volleyball players. Methods: Fifty male volleyball players competing in the Italian Serie C championship were equally distributed across five roles: middle blockers (MB), liberos (LIB), opposite hitters (OH), setters (SET), and outside hitters (HIT). Using a wearable inertial sensor, athletes performed bipodalic balance tasks with eyes open and closed, dominant- and non-dominant-leg single-leg balance, squat jump (SJ), countermovement jump (CMJ), and bilateral upper-limb flexion and extension tests. Results: Significant role-related differences emerged in balance and jump performance. In bipodalic balance, the eyes-open condition showed a mixed pattern, with HIT displaying the largest ellipse area and SET showing the highest path-related values, whereas in the eyes-closed condition, HIT showed the highest values across all stabilometric parameters. In the single-leg stance, OH showed the largest postural excursions on the dominant side, while LIB stood out on the non-dominant side. In jump tests, MB showed the best vertical performance in both SJ and CMJ, whereas LIB and SET generally showed the lowest outputs. Temporal differences also emerged across roles. Upper-limb mobility was similar across roles in flexion, while extension showed a role-specific pattern, with SET displaying greater ROM than LIB, HIT, and OH. Conclusions: Volleyball roles are associated with distinct functional profiles in balance, jump mechanics, and upper-limb mobility. This integrated assessment may support more specific training, monitoring, and injury-prevention strategies. Full article
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14 pages, 678 KB  
Article
Machine Learning-Based Prognostic Prediction for Knee Osteoarthritis After High Tibial Osteotomy Using Wavelet-Derived Gait Features
by Koji Iwasaki, Kento Sabashi, Hidenori Koyano, Yuji Kodama, Shigeyuki Sakurai, Kengo Ukishiro, Ryusuke Ito, Hisashi Matsumoto, Yuichiro Abe, Noriaki Mori, Chiharu Inoue, Yasumitsu Ohkoshi, Tomohiro Onodera, Eiji Kondo and Norimasa Iwasaki
J. Funct. Morphol. Kinesiol. 2026, 11(1), 94; https://doi.org/10.3390/jfmk11010094 - 26 Feb 2026
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Abstract
Background: Osteotomy around the knee (OAK) is a joint-preserving surgery for knee osteoarthritis, yet some patients experience suboptimal outcomes. Preoperative identification of high-risk patients remains challenging. This study aimed to develop a machine learning model to predict clinical outcomes after OAK using [...] Read more.
Background: Osteotomy around the knee (OAK) is a joint-preserving surgery for knee osteoarthritis, yet some patients experience suboptimal outcomes. Preoperative identification of high-risk patients remains challenging. This study aimed to develop a machine learning model to predict clinical outcomes after OAK using preoperative gait acceleration data from inertial measurement units (IMUs). Methods: This multicenter prospective study enrolled patients undergoing OAK. Preoperative gait was recorded using synchronized IMUs placed on the lumbar spine and tibia. Lumbar and tibial signals were used for gait-cycle segmentation, while wavelet-based time–frequency features were extracted from tibial acceleration only. Outcomes were defined by achievement of the minimal clinically important difference in ≥3 KOOS subscales at 2-year follow-up (Good vs. Poor). Continuous wavelet transform features (5–20 Hz) were summarized as mean and standard deviation across six stance subphases. A Random Undersampling Boost classifier was trained and evaluated using nested leave-one-subject-out cross-validation. A sensitivity analysis using logistic regression confirmed that the IMU-based prediction score was independently associated with outcome after adjustment for baseline KOOS (p = 0.047). Results: Of 67 enrolled patients, 37 were classified as Good and 30 as Poor outcome. For machine learning analysis, 1173 tibial acceleration gait-cycle waveforms were usable. The model achieved an AUC of 0.744 (95% CI, 0.610–0.860) using a median of 15 features (range, 5–25) with sensitivity of 0.69 and specificity of 0.72. The most informative predictors were the mean magnitude in the 5–8 Hz band during loading response (0–17%) and variability in the 5–8 Hz band during late stance (67–83%). No significant differences in baseline demographics or radiographic parameters were found between outcome groups. Conclusions: Preoperative IMU-derived gait acceleration features showed moderate-to-good discrimination between outcome groups and may support preoperative risk stratification and individualized perioperative management. Full article
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12 pages, 1289 KB  
Article
Visual Search Behavior During Toileting in Older Patients During the Action-Planning Stage
by Lisa Sato, Naoto Noguchi, Munkhbayasgalan Byambadorj, Ken Kondo, Ryoto Akiyama and Bumsuk Lee
J. Funct. Morphol. Kinesiol. 2025, 10(4), 429; https://doi.org/10.3390/jfmk10040429 - 5 Nov 2025
Viewed by 1331
Abstract
Background: Visual search supports action planning and target selection in daily life. Despite toileting being frequent yet high-risk in rehabilitation, gaze analyses specific to toileting remain limited. This study quantified visual search behavior during the approach phase of toileting. Methods: Twenty [...] Read more.
Background: Visual search supports action planning and target selection in daily life. Despite toileting being frequent yet high-risk in rehabilitation, gaze analyses specific to toileting remain limited. This study quantified visual search behavior during the approach phase of toileting. Methods: Twenty inpatients aged 65 years or older in a convalescent rehabilitation ward participated in the study. At the time of hospital admission, their gaze behavior from toilet room entry to arrival at the bowl was recorded using an eye tracker (Tobii Pro Glasses 2). Moreover, we evaluated a toilet-functional independence measure (toilet-FIM), comprising toileting, toilet transfer, and locomotion at discharge. Results: In multiple regression, a longer total gaze time directed towards the toilet seat was associated with a greater toilet-FIM independence (β = 0.446), whereas prolonged gaze to the toilet rim (β = −0.839) and to the right handrail (β = −0.621) were related to lower independence (adjusted R2 = 0.715). Conclusions: A toilet seat-oriented gaze implies effective action planning for safe sit-down, whereas toilet rim- or handrail-oriented gazes may reflect responses to visual salience or compensatory visual strategies related to reduced independence. These observations could improve our understanding of older patients’ motor planning and spatial perception in toileting. Full article
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