Sensors

A Wearable Monitoring System (WMS), comprising a chest patch, wrist-worn pulse oximeter, and arm-worn blood pressure device, was developed in preparation for a pilot Randomised Controlled Trial (RCT) on a UK surgical ward. The system was designed to support continuous physiological monitoring and early detection of deterioration. An initial prototype user interface was developed by the research team based on prior clinical experience and engineering knowledge. To ensure suitability for clinical practice, iterative user-centred refinement was undertaken through a series of clinician focus groups and wearability assessments. Six focus groups were conducted between November 2019 and May 2021 involving multidisciplinary healthcare professionals. Feedback from these sessions informed successive interface and system modifications. System development spanned the COVID-19 pandemic, during which the WMS was rapidly adapted and deployed to support clinical care on isolation wards. Feedback obtained during this period was incorporated into later versions of the system and provided a unique opportunity to examine changes in clinician priorities under pandemic conditions. Clinicians consistently prioritised alert visibility, alarm fatigue mitigation, parameter flexibility, and centralised monitoring. Notably, preferences regarding alert modality and access mechanisms evolved over time: early enthusiasm for mobile or smartphone-type devices shifted towards a preference for fixed, ward-based displays and audible alerts at the nurses station following pandemic deployment. Building on previous wearability testing in healthy volunteers, wearability testing using a validated questionnaire was completed by 169 patient participants during the RCT. The chest patch and pulse oximeter demonstrated high tolerability, whereas the blood pressure cuff showed poor wearability and was removed from the final system. These findings demonstrate the importance of iterative, clinician-led design for wearable WMS and highlight how extreme clinical contexts such as the COVID-19 pandemic can significantly reshape perceived requirements for safety-critical monitoring technologies.

Continuous, non-invasive cardiovascular monitoring is limited by the superficial sensing depth of Photoplethysmography (PPG), which is susceptible to peripheral artifacts. This study evaluates a wearable dual-modality prototype integrating dryelectrode Impedance Plethysmography (IPG) and PPG within a smartwatch form factor. Results from a pilot study (N=2) demonstrate that IPG signals exhibit a temporal lead over PPG across ventral and dorsal sites, supporting its greater penetration depth. During brachial artery modulation, IPG showed superior sensitivity to arterial recovery on the ventral forearm. Furthermore, 60-minute napping sessions revealed that while PPG remained morphologically stable, IPG signals underwent significant evolution, capturing distinct pulsewave archetypes. These findings suggest that wearable IPG provides a high-fidelity window into deep systemic hemodynamics typically reserved for clinical instrumentation.

Parkinson's Disease (PD) is a complex neurodegenerative disorder that manifests through systemic, large-scale physiological reorganizations. While research often focuses on region-specific neural changes, there is a growing need for multidomain approaches to capture the complexity of the disease and its clinical heterogeneity. This study proposes an analytical pipeline to evaluate Brain-Heart Interplay (BHI) as a novel systemic biomarker for neurodegeneration and healthy ageing. In this study we assessed BHI across three open-source datasets (EEG and ECG signals). We compared Healthy Young, Healthy Elderly, and PD patients in resting state to investigate the effects of ageing and cognitive performance. Additionally, we studied BHI trends in PD patients in the moment of freezing of gait (FOG). Methodologically, brain network organization was quantified using coherence-based EEG connectivity and graph theory, while heart activity was analyzed through Poincare plot-derived measures of cardiac autonomic activity. The coupling between these two systems was measured using the Maximal Information Coefficient to capture linear and non-linear dependencies between global cortical organization and cardiac autonomic outflow. The results demonstrate that BHI is a sensitive biomarker for detecting early multisystem dysfunction in both neurodegeneration and ageing. Furthermore, the identification of specific BHI trends during FOG onset suggests new opportunities for understanding the physiological mechanisms driving motor complications in PD. Our proposed pipeline provides a guiding tool for large-scale physiological assessment in clinical research.

Electroencephalography (EEG) preprocessing is a critical yet time-consuming step that often relies on expert-driven, semi-automatic pipelines, limiting scalability and reproducibility across large datasets. In this work, we present sEEGnal, a fully automated and modular pipeline for EEG preprocessing designed to produce outputs comparable to expert-driven analyses while ensuring consistency and computational efficiency. The pipeline integrates three main modules: data standardization following the EEG extension of the Brain Imaging Data Structure (BIDS), bad channel detection, and artifact identification, combining physiologically grounded criteria with independent component analysis and ICLabel-based classification. Performance was evaluated against manual preprocessing performed by EEG experts at two complementary levels: preprocessing metadata (bad channels, artifact duration, and rejected components) and EEG-derived measures. In addition, test-retest analyses were conducted to assess the stability of the pipeline across repeated recordings. Results show that sEEGnal achieves performance comparable to expert-driven preprocessing while preserving key neurophysiological features. Furthermore, the pipeline demonstrates reduced variability and increased consistency compared to human experts. These findings support sEEGnal as a robust and scalable solution for automated EEG preprocessing in both research and large-scale applications. HighlightsFully automated and modular EEG preprocessing pipeline. Benchmarked against expert-driven preprocessing. Comparable performance in metadata and EEG-derived measures. Demonstrates stable performance in test-retest recordings. BIDS-based framework for reproducible EEG data handling.

BackgroundPatients who have undergone Anterior Cruciate Ligament Reconstruction (ACLR) have a 6-24% chance of either re-tearing or having subsequent knee surgery. To date there have been no practical validated risk prediction models that can be easily implemented into clinical workflow for re-injury risk. Micro-Doppler radar (MDR) provides a promising solution. ObjectiveThe purpose of this study was to investigate the predictive ability of MDR to identify persons with a previous ACLR relative to an age and sex matched healthy control. MethodsACLR patients (n=81) and controls (n=100) performed drop box jump, sit to stand (STS), and walking trials as MDR signatures were collected. A 1D Convolutional Neural Network was developed to evaluate each activity individually followed by the development of a fusion model validation using all three activities. ResultsThe STS model individually achieved the highest overall accuracy of 82.3%, with a sensitivity of 71.6% and specificity of 91.0%. The fusion model using all activities achieved a peak overall accuracy to detect ACLR of 86.2%, 80.3% sensitivity, and 91% specificity. ConclusionsCurrently, there is no clinically validated, efficient approach to objectively evaluate human motion at the point of care. When coupled with machine learning, MDR accurately differentiates ACLR from control groups by identifying complex biomechanical asymmetries, with classification performance comparable to or exceeding that of motion capture. Future research is needed to determine if MDR can be used in conjunction with risk prediction modeling. Key pointsMicro-Doppler radar provides a promising new solution to identify important human motion asymmetries in clinical settings. Here we evaluated a group of patients who have a history of Anterior Cruciate Ligament reconstruction versus a control group. Simple movements performed in the presence of the micro-Doppler radar system were used to identify the 2 groups with accuracy comparable or superior to motion capture systems.

We are pleased to submit our Original article entitled "Assessing medication-related burden and medication adherence among older patients from Central Nepal: A machine learning approach" for consideration in your esteemed journal. In this paper, we assessed medication burden using validated Living with medicines Questionnaire (LMQ-3) and medication adherence using Adherence to Medication refills (ARMS) Scale. In this paper we analysed our result through machine learning approach in spite of traditional statistical approach to identify the complex factors influencing both. Six ML architectures (Ordinary Least Square, LightGBM, Random Forest, XGBoost, SVM, and Penalized linear regression) were employed to predict ARMS and LMQ scores using various socio-demographic, clinical and medication-related predictive features. Model explainability was provided through SHAP (Shapley Additive exPlanations). Our study identified the moderate medication burden with moderate non-adherence among older adults. Requiring assistance for medication and polypharmacy were the strongest drivers for the medication burden and non-adherence. The high predictive accuracy by ML suggests the appropriate clinical intervention like deprescribing to cope with the high prevalent medication burden and non-adherence among older adults in Nepal.

Road traffic accidents represent a global public safety crisis, necessitating advanced computational tools for accurate injury severity prediction and effective decision support. This study evaluates high-performing ensemble machine learning models, including AdaBoost, XGBoost, LightGBM, HistGBRT, CatBoost, Gradient Boosting, NGBoost, and Random Forest, using a comprehensive National Highway Traffic Safety Administration (NHTSA) dataset from 2018 to 2022. While all models demonstrated exceptional predictive accuracy, with HistGBRT achieving the highest overall accuracy of 92.26%, a defining achievement of this work is the perfect classification (100% precision and recall) of fatal injuries across all ensemble architectures. To bridge the gap between predictive performance and actionable intelligence, this research integrates SHapley Additive exPlanations (SHAP) to provide both global insights into dataset-wide risk factors and local, instance-specific rationales for individual crash events. The global analysis identified ethnicity, airbag deployment, and harmful event type as primary drivers of injury severity, while local force and waterfall plots revealed the precise "push and pull" of variables for specific incidents. The results offer a robust, interpretable framework for stakeholders tasked with improving traffic safety and mitigating crash-related harm.

Conflicting evidence on scatter correction (SC) methods plagues quantitative myocardial perfusion SPECT (MPI), hindering standardized clinical protocols. This simulation study, utilizing the SIMIND Monte Carlo program and a highly realistic 4D XCAT phantom, systematically evaluates Dual Energy Window (DEW, with k=0.5) and Triple Energy Window (TEW) SC techniques. We uniquely investigate their performance across various photopeak window widths (2, 4, and 6 keV) and novel overlapped/non overlapped configurations specifically for Tc 99m MPI parameters largely unexplored in realistic cardiac models. Images were reconstructed with OSEM under uncorrected (UC), SC, and combined attenuation and scatter corrected (ACSC) conditions. Quantitative analysis focused on signal to noise ratio (SNR), contrast to noise ratio (CNR), defect contrast, and relative noise to background (RNB). Our findings consistently show ACSC's superior performance in CNR, SNR, and defect contrast, confirming its critical role. Interestingly, SC alone reduced noise but compromised defect contrast relative to UC, highlighting a potential trade-off without attenuation correction. Crucially, this study reveals minimal influence of photopeak window width and overlap configuration on image quality, and no significant difference between DEW and TEW across most metrics. These results provide essential evidence for optimizing quantitative MPI protocols, suggesting that for Tc 99m, the choice between DEW and TEW, and specific window settings, may be less critical than ensuring robust attenuation correction.

Increasing human activities along the US west coast are of concern for populations of cetaceans and particularly for a number of large whale species that are recovering from overexploitation during the era of commercial whaling. New rapid monitoring tools, such as satellite imagery analysis powered by recent advances in artificial intelligence, have potential to provide additional broad-scale and near real-time capacities for survey and monitoring. This paper investigates and demonstrates the feasibility of automatic detection of gray whales in sub-meter satellite imagery off the coast of California, USA. Observations and statistical analysis of regional imagery allowed not only an assessment of their detectability but also the development of robust signal processing and machine learning-based solutions for automated detection. To that end, a regional dataset of 221 gray whales was created using signal processing to inform a deep-learning-based detection framework, and 20 different large neural network architectures for feature extraction followed by a support vector machine algorithm for classification were evaluated for their detection performance. Neural network backbones included 19 convolutional neural networks and 1 transformer network. The best architecture generally achieved satisfying performance with an average balanced accuracy reaching up to 99.90%. It is also demonstrated that panchromatic imagery, in spite of the lesser amount of information provided, can be used to perform detection with a relatively high accuracy of 87.05%, allowing wider spatial and temporal coverage. Large-scale deployment of the best performing models over a broad range of regional satellite imagery resulted in the detection of 3353 gray whales, as well as opportunistic detections of humpback, blue and fin whales, in and going from December 28th 2009 to March 26th 2023. It also provided meaningful data points concerning the migration routes of gray whales within the Channel Islands and Southern California Bight. The large number of high-confidence detections indicates the capacity for a large-scale monitoring approach to support state and federal conservation policies such as gear mitigation, vessel speed reduction programs, or shipping lane redefinition that could also be expanded to other areas and for other species.

Neuropathy caused by chemotherapy is a common and debilitating side-effect of cancer treatment. With 30% of patients experiencing chronic neuropathy and with no good evidence-based treatments; early detection triggering chemotherapy regime modification remains the best option for prevention. Early detection is challenging because of a lack of diagnostic tools with sufficient longitudinal temporal precision and convenience for patient/clinical adoption. To tackle this problem, we developed SenseCheQ; enabling self-administered autonomous sensory testing which can be used by patients at home. We present the instrumental engineering approach taken to address the challenge, including haptic self-calibration combined with skin thermal-clamping protocols, and demonstrate robustly reliable performance in the face of environmental and user-related variance in home settings. We present prospective case studies of people having chemotherapy treatment for cancer, conducting regular unsupervised quantitative sensory testing to monitor their nerve function at home. These proof-of-principle studies show SenseCheQ can detect sub-clinical changes in nerve function, matching patient reported outcomes and lab-based sensory testing. This highlights SenseCheQs promise as a scalable biomarker platform for neuropathy-detection and therapeutic development.

PurposeQuantification of walking function, including joint motions, ground reactions, and joint loads, outside the lab is a growing research area. Because only joint motions can currently be measured outside the lab, researchers are utilizing tracking optimizations of walking to estimate associated ground reactions and inverse dynamic joint loads. However, foot-ground contact models used in such optimizations have been generic rather than personalized, which may limit the accuracy of estimated ground reactions and joint loads. This study compares the predictive capabilities of generic versus personalized foot-ground contact models. MethodsGeneric and personalized foot-ground contact models were evaluated in calibration and tracking optimizations performed using experimental walking data collected from three subjects in varying states of health. Foot-only calibration optimizations evaluated how well both models could reproduce experimental ground reaction and foot motion data while tracking both types of data simultaneously, while whole-body tracking optimizations evaluated how well both models could reproduce experimental ground reactions, joint motion, and joint load data while tracking only experimental joint motion data and achieving dynamic consistency. ResultsFor all three subjects and both types of optimizations, personalized foot-ground contact models reproduced experimental ground reaction, joint motion, and joint load data more accurately than generic foot-ground contact models. ConclusionPersonalized foot-ground contact models can improve the accuracy with which ground reactions and joint loads can be estimated via tracking optimizations of walking using only experimental motion data as inputs. Personalized models require little time and effort to calibrate using freely available software tools and should improve the accuracy of predictive simulations of walking as well.

Patients with amyotrophic lateral sclerosis (ALS) face critical decisions regarding life-sustaining treatments, such as invasive mechanical ventilation and percutaneous endoscopic gastrostomy. Advance care planning and shared decision-making are standard supportive frameworks but they often fail to account for structural pressures like progressive decline, shifting patient values, and fear of becoming a burden that may influence decision-making. This study explores how patients with ALS interpret ventilator and care options amid progressive physical decline, thereby reconsidering approaches to decision support. Using a qualitative descriptive design, the researcher (a nurse/sociologist) conducted 2-3 hour home interviews with five purposively sampled patients with ALS. Data, including eye-tracking-aided responses, were analysed via Sandelowskis framework. Rigour was ensured through team-based triangulation, independent coding by two researchers, and a reflexive audit trail. Subjective narratives were prioritised without medical record cross-referencing to capture patients experiences. Four categories emerged: (1) Rewriting clinical prognosis into a narrative of exploration via peer models, where meeting active ventilator users transformed future perceptions; (2) The conflict between securing care infrastructure and the burden on family, which greatly influenced the will to survive; (3) Existential fluctuation, where patients intentions shifted with daily fulfilment and family events; and (4) Governance of the body via pre-emptive technology use and training carers as physical extensions. Findings showed decision-making was a multi-layered process redefining lifes meaning within social resources. This necessitate shifting from independent to relational autonomy, where agency relies on care infrastructure, not physical ability. Treatment choice is a dynamic exploration requiring narrative companions to support existential fluctuations. Professionals must coordinate environments to reduce patient indebtedness. Limitations include the small, resource-advantaged sample (N = 5) and reliance on subjective narratives without medical record verification. Living with ALS means governing a new self through relational support and continuous dialogue.

Background: Radiographic detection of caries lesions adjacent to restorations is challenging due to limitations of two-dimensional imaging and difficulties distinguishing true lesions from restorative or anatomical radiolucencies. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) have been introduced to assist radiographic interpretation; however, different AI tools may yield variable diagnostic outputs, and their comparative performance remains unclear. Objective: To compare the diagnostic performance of commercial and experimental AI algorithms for detecting secondary caries lesions on bitewings. Methods: This cross-sectional diagnostic accuracy study included 200 anonymized bitewings comprising 885 restored tooth surfaces. A consensus group reference standard identified all surfaces with a caries lesion and classified each lesion by type (primary/secondary) and depth (enamel-only/dentin-involved). Five commercial (Second Opinion, CranioCatch, Diagnocat, DIO Inteligencia, and Align X-ray Insights) and three experimental (Mask R-CNN-based and Mask DINO-based) systems were tested. Diagnostic performance was expressed through sensitivity, specificity, and overall accuracy (95% CI). Comparisons used generalized estimating equations, adjusted for clustered data. Results: Specificity was high across all systems (0.957-0.986), confirming accurate recognition of non-carious surfaces, whereas sensitivity was moderate (0.327-0.487), reflecting frequent missed detections of enamel and dentin lesions. Accuracy ranged from 0.882 to 0.917, with no significant differences among models (p >= 0.05). Confounding factors, such as radiographic overlapping, marginal restoration defects, and cervical artifacts, were the main sources of misclassification. Conclusions: AI algorithms, regardless of architecture or commercial status, showed similar diagnostic capabilities and a conservative detection profile, favoring specificity over sensitivity. Improvements in dataset diversity, labeling precision, and explainability may further enhance reliability for secondary caries detection. Clinical Significance: AI-based CDSSs assist clinicians by providing consistent detection. Their high specificity is particularly valuable in minimizing unnecessary invasive treatments (overtreatment), though they should be used as adjuncts rather than a replacement for expert judgment.

Research in cognitive neuroscience has relied on simple, highly controlled stimuli due to the difficulty in developing standardized, ecologically valid stimulus sets. However, there is a consensus that using ecologically valid stimuli is imperative to generalize results beyond controlled laboratory settings. The current study introduces a naturalistic audio stimulus database, consisting of short, recognizable, and emotionally rated stimuli. To create such a database, the current study collected 291 audio files from a wide range of sources. 361 participants rated the audio clips on emotionality, arousal, and recognizability, and subsequently freely described the audios by typing what they believed the sound to be. The text responses of the participants were embedded and clustered using an unsupervised machine-learning algorithm to derive a participant-grounded organization of auditory object categories. The results indicate audio clips were easily recognizable, while emotionality and arousal ratings showed broad variability, making the database suitable for diverse experimental needs. Furthermore, the final database comprises 10 distinct semantic categories, providing a diverse set of auditory stimuli.

Despite the remarkable progress of artificial intelligence represented by large language models, how AI technologies can contribute to the construction of evidence in evidence-based medicine (EBM) remains an overlooked issue. Now, we need an AI that can be compatible with EBM. In the present paper, we aim to propose an example analysis that may contribute to this approach using variable Vision Transformer.

Radiation-induced intestinal injury is a widely used model for studying mechanisms regulating tissue injury and regeneration. Traditionally, Cesium (137Cs) radiation has been used in research applications, but over the past decade, X-ray irradiation has become increasingly favored due to its improved safety and non-radioactive profile. Since each type of radiation has distinct physical characteristics that drive its performance, we sought to systematically compare the effects of the X-ray and 137Cs irradiators on intestinal epithelial injury and regeneration. Using established in vitro models, including colorectal cancer cell lines such as HCT116, RKO, and DLD-1, and mouse intestinal organoids, alongside an in vivo model, Bmi1-CreER;Rosa26eYFP, we evaluated differences in transcriptional, protein, and histopathological responses to irradiation. Our results demonstrate that X-ray produced intestinal injury and regenerative responses comparable to those induced by 137Cs, supporting its reliability as an alternative modality for studying intestinal radiation.

There is more to musculotendon path modeling than aligning a cable to reflect the geometric features of a muscle-tendon unit. From the perspective of simulation accuracy, the key is to replicate the length- and moment arm-joint angle relations of the target muscle. In this study, we propose an effect-oriented approach of automated path modeling, via the hybrid calibration based on muscle surface mesh and moment arm. The task is formulated as an optimization problem with a threefold objective for the path to: 1) pass through multiple ellipses representing muscle cross-sections, 2) yield moment arms that match experimental measurements, and 3) yield moment arms with the designated signs. The performance of our optimization framework is demonstrated with the musculoskeletal surface mesh from the Visible Human Male and moment arm datasets from literature--producing 42 paths that are anatomically realistic and biomechanically accurate in 20.1 min. Our optimization framework is gradient-specified, which is faster and more accurate than using the default numerical gradient, making it applicable for large-scale subject-specific uses.

Ambulatory electrocardiograms (ECG) provides continuous monitoring of the hearts electrical activity. However, many existing machine learning and artificial intelligence models for analyzing ambulatory ECG traces are often unimodal and do not incorporate patient clinical context. In this study, we propose a multimodal framework integrating ambulatory ECG-derived representations with clinical text embeddings to predict two cardiac outcomes: sudden cardiac death and pump failure death. Ambulatory ECG traces are preprocessed, segmented, and encoded via a multiple instance learning and temporal convolutional neural network framework. In parallel, patient clinical features are parsed into structured prompts, which are passed through a large language model to generate clinical reasoning; this reasoning passes through a biomedical language encoder to generate a text embedding. With the ECG and text embeddings, we systematically evaluate multiple fusion strategies, including concatenation- and gating-based approaches, to integrate these two data modalities. Our results demonstrate that multimodal models consistently outperform unimodal baselines, with adaptive fusion mechanisms providing the greatest improvements in predictive performance. Decision curve analysis highlights the potential clinical utility of the proposed framework for risk stratification. Finally, we visualize model attention across modalities, including ECG attention patterns, segment-level saliency, heart rate variability features, and clinical reasoning, to contextualize patient-specific predictions.

Three-dimensional dual-energy X-ray absorptiometry (3D-DXA) reconstructs proximal femur models from standard scans to estimate cortical and trabecular bone parameters. The aim of this study was to evaluate 3D-DXA against quantitative computed tomography (QCT) across independent international cohorts. The study included 537 subjects from four cohorts: an adult population from Spain, a postmenopausal female population from the United States, an osteoarthrosis population and a young population, both from Japan. Subjects underwent both 3D-DXA and QCT imaging. Accuracy was assessed using linear regression and Bland-Altman analysis to evaluate systematic and random errors. 3D-DXA parameters strongly correlated with QCT across all datasets, with correlation coefficients between 0.82 and 0.97. Random errors were consistent across cohorts and ranged between 16.55 and 19.91 mg/cm3 for integral volumetric bone mineral density (vBMD), between 13.52 and 18.47 mg/cm3 for trabecular vBMD, and between 9.13 and 11.37 mg/cm2 for cortical surface bone mineral density (sBMD). Systematic errors ranged between -14.84 and 4.50 mg/cm3 for integral vBMD, between -8.31 and 14.41 mg/cm3 for trabecular vBMD, and between -5.58 and 3.21 mg/cm2 for cortical sBMD. The variations in systematic errors were likely attributable to differences in QCT acquisition protocols. Overall, these results demonstrate consistent agreement between 3D-DXA and QCT across sex, age, ethnicity, geographic regions, and clinical profiles. Taken together, these findings support the use of 3D-DXA as an accurate, non-invasive, and clinically accessible technology for advanced assessment of the cortical and trabecular compartments of the proximal femur.

Purpose: This study aimed to examine the effects of formative and summative assessments on college students tennis performance and basic psychological needs. Methods: A total of 128 undergraduate students (64 males, 64 females; Mage = 19.22, SD = 0.91) participated in this study. Participants were cluster-randomized to either a formative assessment group (n = 64) or a summative assessment group (n = 64). The formative assessment intervention involved setting personalized learning goals and success criteria, administering periodic tests, and providing process-oriented and individualized feedback. The summative assessment intervention involved setting uniform goals for all students, offering instructor feedback only on common problems, and requiring students to practice independently after class without personalized guidance. Both interventions were implemented over 10 weeks, with one 90-minute session each week. Tennis skills and basic psychological needs (i.e., autonomy, competence, and relatedness) were assessed before and after the intervention. Tennis skills were reassessed 1 week after the intervention. Two-way mixed-effects analysis of variance (ANOVA) was used to examine the impact of group, time, and their interaction on tennis skills and basic psychological needs. Results: The results showed that the interaction between group and time was significant for all of the outcome variables. Simple effects analyses indicated that, at pre-test, the two groups did not differ significantly in tennis performance or in satisfaction of autonomy, competence, and relatedness (p > 0.05). At post-intervention, the formative assessment group demonstrated significantly better performance than the summative assessment group in tennis skills (MD = 3.50, 95% CI = [1.303, 5.697], p = 0.002), autonomy (MD = 2.44, 95% CI = [1.816, 3.059], p < 0.001), relatedness (MD = 1.33, 95% CI = [0.679, 1.977], p < 0.001), and competence (MD = 1.75, 95% CI = [1.046, 2.454], p < 0.001). At the 1-week follow-up session, the formative assessment group also showed significantly better tennis performance than the summative assessment group (MD = 6.81, 95% CI = [4.667, 8.958], p < 0.001). Conclusion: Formative assessment was more effective than summative assessment in improving college students tennis performance and satisfying their basic psychological needs. These findings suggest that incorporating personalized goals, process-oriented evaluation, and individualized feedback into tennis instruction could promote both skill development and psychological outcomes in college physical education.