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

The central challenge the authors confront is that pulsed laser deposition is a strongly nonlinear, fast-changing process. When an excimer or femtosecond laser pulse, typically lasting tens of nanoseconds, is absorbed by the target surface, the material does not evaporate gently. Instead, it is ejected in a plasma plume whose composition, density, temperature and velocity depend sensitively on fluence, spot size, target morphology and background gas pressure. Downstream, the plume interacts with oxygen or nitrogen atoms, thermalizes, and finally condenses on the substrate, where surface diffusion, nucleation and growth kinetics decide whether the film crystallizes in the desired phase. Small drifts anywhere along this chain, such as target roughening after thousands of shots or a subtle shift in plume stoichiometry, can push the film away from its intended properties without any obvious warning until post-deposition characterization reveals the damage.

Traditional control approaches have largely relied on static recipes: fix the laser fluence, the repetition rate, the background pressure and the substrate temperature, and hope the recipe is robust enough to survive normal process variability. Some facilities have added monitoring, such as optical emission spectroscopy of the plume or reflection high-energy electron diffraction at the substrate, but the information gathered is usually used for post hoc diagnostics rather than for closed-loop correction. The new work argues that this is precisely the gap where modern data-driven methods can make a decisive difference. By learning a model of the process-state dynamics directly from real-time measurements, the controller can predict where the process is heading and act before deviations accumulate into defects.

The heart of the framework is a predictive model of the process state, constructed from data acquired during deposition rather than from first-principles simulation alone. The process state is a compact mathematical description of the quantities that actually govern film formation, including indicators of plume behavior, ablation dynamics and growth conditions at the substrate. Instead of requiring an engineer to derive governing equations by hand, the approach trains a model to map the recent history of process measurements and actuator settings onto the future evolution of that state. This model predictive structure then solves, at every control step, an optimization problem: choose the adjustments to the manipulated variables, such as laser fluence or timing, that drive the predicted process trajectory toward the desired reference while respecting physical and hardware constraints.

Predictive feedback of this kind has a crucial advantage over purely reactive control. A reactive controller waits for the measured state to deviate and then corrects, which is often too slow in a process whose characteristic timescales are milliseconds to seconds and whose nonlinearities can amplify errors. A predictive controller, by contrast, anticipates the deviation several steps ahead. If the model indicates that the plume conditions are drifting toward a regime associated with off-stoichiometric growth, the controller can modulate the laser energy before that regime is reached. This anticipatory action smooths transients, suppresses oscillations and keeps the process state within the corridor where the intended film phase forms reliably.

The data-driven element is equally important for practical deployment. Physical models of laser ablation and plume transport exist, but they are computationally expensive, parameter-sensitive and awkward to embed in a controller that must make decisions in real time. Learned models, in contrast, can be trained on the actual deposition system in question, capturing chamber-specific quirks that no generic simulation would encode. The authors emphasize that the framework treats the learned model as a surrogate for the true process dynamics and wraps it in the well-established machinery of model predictive control, which brings guarantees of constraint handling and stability that pure black-box machine-learning controllers often lack. The result is a hybrid: machine learning supplies the speed and adaptability, while predictive control theory supplies the discipline.

Real-time implementation is the aspect that makes the work notable for manufacturing rather than merely for laboratory science. Thin-film deposition for quantum devices, power electronics, sensors and photonic components increasingly demands not just one good film but reproducible good films, batch after batch, tool after tool. Variability between depositions is a persistent tax on yields and costs. A controller that senses the process state continuously and corrects drift automatically reduces dependence on operator expertise, shortens the tuning time for new materials, and offers a pathway to qualification of deposition processes in industrial settings where every shot of the laser must count. In effect, the technique moves pulsed laser deposition closer to the kind of statistically controlled, closed-loop manufacturing that semiconductor fabs take for granted.

The implications extend beyond the specific chamber geometry or material system studied. Pulsed laser deposition is prized for complex oxides, where perovskites, superconductors and magnetic heterostructures must be grown with layer-by-layer control of cation stoichiometry. It is also central to emerging applications such as solid-state battery electrolytes, ferroelectric memories and wide-bandgap semiconductor films. Each of these areas suffers from the same underlying difficulty: the relationship between process parameters and film quality is mediated by a fast, chaotic intermediate stage, the ablation plume, which no one can observe directly at the substrate with full fidelity. A predictive state-space controller sidesteps part of this difficulty by inferring the unobservable state from accessible measurements, much as modern automotive control systems infer combustion dynamics from a handful of sensors.

The work also illustrates a broader trend in advanced manufacturing: the migration of machine learning from offline analysis, where models are trained on historical process data to predict yield or classify defects, into the tight feedback loop of the equipment itself. In offline mode, machine learning informs the engineer; in closed-loop mode, it acts. That transition raises the bar for reliability, interpretability and safety, and it is why the authors pair their learned dynamics model with the constrained optimization structure of predictive control. Constraint handling matters in a real deposition tool: the laser fluence cannot exceed what the optics and target tolerate, the repetition rate is bounded by plume clearance times, and abrupt setpoint changes can itself induce defects. A controller that respects these limits while optimizing performance is far more credible on a production floor than one that merely minimizes a loss function on paper.

Looking forward, the authors’ framework suggests a future in which deposition tools continuously learn from every film they grow. Each deposition adds data that can refine the model of process-state dynamics, making the controller better calibrated for the next run, the next material or the next chamber condition. Combined with increasingly rich in-situ diagnostics, such as plume imaging, spectroscopic monitoring and real-time film thickness gauges, predictive feedback control could reduce the development cycle for new functional materials from months of parameter sweeps to a handful of instrumented runs. For a technique that helped pioneer the growth of some of the most celebrated quantum materials of the past three decades, that would represent a quiet but profound modernization: the same flashes of laser light, now guided by an algorithm that watches, predicts and corrects thousands of times faster than any human ever could.

Subject of Research: Real-time data-driven predictive feedback control of process-state dynamics in pulsed laser deposition thin-film growth

Article Title: Real-time data-driven predictive feedback control of process-state dynamics for pulsed laser deposition

Article References: Horide, T., Kawabata, K., & Yoshida, Y. (2026). Real-time data-driven predictive feedback control of process-state dynamics for pulsed laser deposition. npj Advanced Manufacturing, 3(1), Article 28. https://doi.org/10.1038/s44334-026-00112-w

Image Credits: AI Generated

DOI: 10.1038/s44334-026-00112-w

Keywords: pulsed laser deposition, predictive control, model predictive control, machine learning, thin films, advanced manufacturing, laser ablation, real-time feedback, process control, complex oxides, npj Advanced Manufacturing, Real-time

Cardiac arrest remains one of the most time-critical emergencies in medicine. Survival depends overwhelmingly on what happens in the first minutes, and high-quality CPR, delivered with correct posture, depth and sequence, is one of the few interventions bystanders can provide before emergency services arrive. Yet training outcomes vary widely, and studies cited by the authors point to persistent challenges in nursing education and public awareness campaigns across Europe. Traditional CPR courses rely on instructors observing multiple trainees at once, which makes fine-grained, continuous correction of every compression and hand position practically impossible. The new system was designed to fill that gap by acting as a tireless, always-attentive digital assistant that evaluates every movement the moment it happens.

The technical core of the system is a two-stage pipeline. The first stage uses HRNet, a deep neural network architecture for human pose estimation that maintains high-resolution representations throughout its processing stages. Unlike architectures that downscale images and then attempt to recover spatial detail, HRNet preserves fine-grained information about body joints, which is essential for CPR, where subtle deviations in arm angle or shoulder alignment can mean the difference between effective and ineffective chest compressions. The network converts a standard camera feed into a skeletal representation of the trainee, a stick-figure abstraction that captures where the head, shoulders, elbows, wrists, hips and knees are in each frame.

The second stage takes those skeleton sequences and classifies what the trainee is actually doing. The researchers employed ST-GCN++, an improved version of the Spatio-Temporal Graph Convolutional Network, a class of models built specifically for skeleton-based action recognition. A human skeleton is naturally a graph: joints are nodes, and bones are edges. A graph convolutional network propagates information across that graph, learning patterns both in space, how the joints relate to each other in a single frame, and in time, how those relationships evolve across a sequence of frames. The “plus-plus” refinement adds improved backbone design and multi-stream inputs, boosting accuracy without requiring enormous computational resources.

To make the system work in real time, the team segmented the streaming video into short clips, classified the CPR actions on the fly, and compared the recognized action sequence against the standard CPR procedure. This is where the “process-aware” component comes in. The system does not merely ask whether a given movement resembles a chest compression; it tracks whether the actions are happening in the correct order, whether the posture matches the protocol, and whether the trainee is hesitating. When execution order or posture deviates from the standard procedure, the system immediately triggers audio-visual prompts, essentially coaching the learner in the moment rather than after the fact. The architecture is deliberately lightweight and runs on commodity hardware, a design decision that opens the door to deployment in schools, community centers, and remote or resource-constrained settings where expensive manikin sensor systems are unavailable.

The machine-learning performance figures are strong. Evaluated with randomized segment-level splits, the action classifier achieved an accuracy of 0.93 and a recall of 0.93, with a balanced accuracy of 0.91 and a macro-averaged F1-score of 0.91. The use of balanced accuracy and macro-averaged F1 indicates the team was attentive to class imbalance, ensuring the model performs well across all CPR action categories rather than excelling only on the most common ones. The researchers framed the entire work as a controlled feasibility study, and were careful to note that the model evaluation used segment-level randomization, a detail that matters because action-recognition models can sometimes exploit temporal redundancy between adjacent clips if data splitting is not handled properly.

The team then took the system out of the lab’s benchmark datasets and into a quasi-experimental training study. Sixty participants were divided into two groups of thirty. One group received traditional instructor-led CPR training; the other trained with the intelligent feedback system. The entire experiment, including participant briefing, a pre-test, instructor-led instruction, group-based practice, and post-test assessment, was completed over approximately eight hours. Performance after training was assessed by a blinded CPR instructor, who scored each participant on action sequence correctness and hesitation duration without knowing which group the participant had belonged to, a design choice that reduces evaluator bias.

The results were unambiguous. The group trained with the intelligent system significantly outperformed the traditional training group in skill execution, with a mean score of 89 versus 72, a difference that was highly statistically significant with p less than 0.001 and a large effect size, reflected in a rank-biserial correlation of 0.606. In practical terms, trainees who received instant AI feedback not only performed better but also hesitated less, suggesting the system helped them internalize the correct procedural sequence rather than forcing them to consciously recall it under pressure. The experimental group also reported lower cognitive load, measured with established psychological instruments, an outcome the authors link to the way immediate, automated feedback relieves learners of the burden of self-monitoring while performing a physically and mentally demanding task.

Usability and satisfaction metrics told a similar story. The system achieved a System Usability Scale score of 78.6, placing it in the upper range of the widely used 0-to-100 usability instrument, and participants expressed greater satisfaction with their training experience compared with the traditional group. The authors suggest that skeleton-based action recognition coupled with real-time multisensory feedback can support practical skill acquisition and learner confidence in a controlled feasibility setting, and that the reduction in cognitive load may be a key mechanism behind the improved learning outcomes, since overload during skill practice is known to impair retention.

The implications extend well beyond CPR. The study’s authors, Ming-Chuan Chiu and Zi-Heng Huang of National Tsing Hua University and Meng-Chun Kao of Yuanpei University of Medical Technology, note that the same skeleton-based approach could be extended, after further validation and runtime profiling, to other skill-based domains, from industrial assembly and physical rehabilitation to surgical technique and workplace safety training. Because the system needs only a camera and commodity computing hardware, it avoids the cost barrier of sensor-instrumented manikins, which could make high-quality, feedback-rich training accessible in low-resource environments and remote learning scenarios where certified instructors are scarce.

The research was supported by the National Science and Technology Council, Taiwan, and approved by the Research Ethics Review Committee of National Tsing Hua University. The team emphasizes that the work remains a feasibility study: larger samples, longer-term retention testing and real-world deployment studies will be needed before the system can be recommended as a replacement for, rather than a supplement to, certified CPR instruction. But the combination of near-human-level action recognition accuracy, measurable improvements in trainee performance, and a lightweight, hardware-agnostic design makes a compelling case that the next generation of life-saving training may not need a human eye on every compression, only a camera, a skeleton, and a graph neural network watching the bones move.