The review followed a rigorous scoping methodology aligned with the PRISMA Extension for Scoping Reviews and the Joanna Briggs Institute Population–Concept–Context framework. The team searched three engineering and computer science databases, Scopus, the ACM Digital Library and IEEE Xplore, for publications between January 2000 and February 2025. In a striking methodological decision, the researchers deliberately excluded the word rhythm itself, along with synonyms such as timing, tempo, flow and cadence, from their search strings. Because rhythm is conceptually complex and used differently across disciplines, searching for it directly would have flooded the results with irrelevant work. Instead, they captured the broad landscape of augmentation and feedback technologies in sport, then examined whether the holistic, expert-understood concept of rhythm was actually being addressed. After screening 442 unique records from an initial pool of 529, the team distilled the literature down to 129 technology-focused studies.

The first headline finding is the era of the wearable sensor. Wearable devices were the primary technology in 48.8 percent of the included studies, and inertial measurement units, the tiny accelerometer and gyroscope packages that detect motion, appeared in 44.2 percent of the entire corpus. Their dominance reflects maturity, low cost and high ecological validity: unlike laboratory motion capture rigs, IMU-based wearables can record rhythm-relevant features such as step frequency, stance duration and inter-movement intervals directly in the field, on the track, in the pool or on the slopes. Examples in the corpus range from flexible sensor networks for sprint and jump biomechanics to smart dumbbells with embedded inertial units that evaluate strength training form in real time. For anyone hoping to quantify the temporal texture of movement, the sensor hardware problem is, in essence, already solved.

The second headline is a turn toward seeing rather than feeling the data. Computer vision systems accounted for 20.2 percent of primary technologies and extended reality systems for 22.5 percent, together representing more than 40 percent of the corpus. Visual capture technologies spanning ordinary RGB cameras, LiDAR and full motion capture appeared in one third of all hardware implementations. This suggests a field diverging into two camps: one that attaches discrete sensors to the body, and another that leverages optics and immersive environments where users quite literally inhabit their own performance data. The rise of extended reality, in particular, signals a shift beyond passive data logging toward experiential interfaces, exemplified by virtual reality training systems evaluated with national-level sitting volleyball players that produced measurable skill improvement.

Underpinning both camps is artificial intelligence. Explicit use of AI or machine learning to generate or adapt feedback appeared in 42 percent of the included studies. Rather than being the primary technology, machine learning frequently serves as the translation layer that converts raw sensor streams into meaningful, actionable cues. Examples range from wearable systems using deep learning to provide adaptive auditory feedback for weight training to computer vision pipelines that assess landing form in basketball. In principle, this is exactly the kind of computational machinery needed to move from simple real-time alerts toward the sophisticated, personalized, adaptive feedback systems that the authors argue elite sport actually requires.

Yet the feedback side of the equation reveals a striking monoculture. Visual feedback dominated the literature, appearing in 81 percent of studies, most commonly as on-screen graphs and numerical displays delivered via smartphone or tablet in 61 percent of the corpus. A representative system for hammer throw training gives coaches a real-time plot of the wire tension curve, elegant for a coach watching but demanding that an athlete look away from their own movement. The authors point out that visual attention during performance is a limited and critical resource, and studies on augmented reality workout systems have found that on-screen cues can impose additional cognitive load during dynamic movement. For an embodied skill like rhythm, which is often felt as much as seen, defaulting to screens may be actively counterproductive.

The untapped opportunity, the review argues, lies in the senses sport has largely ignored. Auditory feedback appeared in only 25 percent of studies and haptic feedback in a mere 11 percent. Their temporal affordances make them natural candidates for rhythm cueing: the ear is a timing organ, and the skin can feel a pulse without any attention being paid. The few studies that do explore these channels hint at real promise. Interactive sonification, in which movement is converted into continuous sound, has provided effective low-latency feedback on body roll in swimming, and electrical muscle stimulation has been used to directly correct running foot-strike patterns. Crucially, the auditory channel often remains unengaged during high-concentration physical exercise, leaving bandwidth free for guidance that refines rather than disrupts an athlete’s flow.

The review also documents a troubling gap between who builds these systems and who is supposed to benefit from them. Elite and professional athletes were the target population in only 26 percent of studies, and coaches in just 18.5 percent, while novices and amateurs, often university students, are heavily over-represented. The most common application domain was general fitness training, and 70 percent of studies targeted posture or technique form, compared with only 35 percent targeting timing or tempo, 20 percent targeting consistency or flow, and 12 percent targeting stride parameters. In other words, the literature measures discrete kinematic snapshots when rhythm is an integrated temporal quality. Elite performers possess refined, idiosyncratic motor programs; their challenge is micro-adjustment of an already optimized system, requiring higher-resolution feedback than novice learning studies typically provide.

The proof problem compounds the population problem. Although 81 percent of systems were empirically evaluated, the primary goal of most evaluations was system usability or feasibility, in 45 percent of studies, while only 22.5 percent measured performance improvement and 8.5 percent measured skill acquisition. Nearly one in five studies, 19.4 percent, reported no formal evaluation at all. Perhaps most tellingly, only 26.5 percent of studies explicitly measured rhythm or a direct synonym as an outcome variable. Systems for tennis swing classification, for example, celebrated machine learning model accuracy rather than any change in player skill. The result is a literature full of working prototypes with largely unevaluated training impact.

The authors distill these observations into four challenges, concerning what is sensed, who is studied, how feedback is delivered and what proof exists, and offer them as exploratory, hypothesis-generating directions rather than evidence-based prescriptions. Their near-term proposal is compelling in its simplicity: couple the already mature wearable inertial sensing platforms with continuous, low-latency, non-visual feedback such as movement sonification or complementary haptic cues, and design evaluations that separate technical validation, user experience and genuine performance outcomes as distinct levels of evidence, a trajectory they map onto the Technology Readiness Level framework from proof-of-concept to proof-of-impact. The review has limitations, including its restriction to English-language, technology-indexed literature across three engineering databases, so field-level conclusions about rhythm training await a cross-disciplinary synthesis. But the message to sports engineers is clear: the sensors are ready, the audio and touch channels are wide open, and the athletes at the top are still waiting for technology that speaks their temporal language.

Subject of Research: Human augmentation technologies and feedback modalities for training rhythm in elite sport

Article Title: Augmenting rhythm: a scoping review of technologies and feedback modalities in elite sport

Article References: Hossain, M. T., Lindeman, R. W., Ingram, M., & Lukosch, S. (2026). Augmenting rhythm: a scoping review of technologies and feedback modalities in elite sport. Sports Engineering, 29(2), Article 32. https://doi.org/10.1007/s12283-026-00562-7

Image Credits: AI Generated

DOI: 10.1007/s12283-026-00562-7

Keywords: rhythm, elite sport, wearable sensors, inertial measurement units, real-time feedback, sonification, haptic feedback, extended reality, computer vision, artificial intelligence, sports engineering, scoping review