The study, led by Urvashi, Rishu, and Vinay Kukreja from the Centre for Research Impact and Outcome at Chitkara University Institute of Engineering and Technology, follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, or PRISMA, guidelines to ensure transparency and reproducibility. The researchers began with an initial pool of 411 articles gathered from Google Scholar, IEEE Xplore, ScienceDirect, and Scopus using structured search strings. Through a rigorous multi-stage screening process that included duplicate removal, title and abstract screening, full-text evaluation, and formal quality assessment, the team narrowed the corpus to fifty studies for in-depth analysis. These selected works were then examined for methodological trends, reported recognition accuracies, dataset usage patterns, the pace of research growth, and validation practices.

The scientific premise underlying this field is not new. Psychologists have long observed that emotional states manifest in body movement. Landmark studies showed that people with depression exhibit slower, less expressive walking patterns, and that observers can correctly attribute emotions such as happiness, sadness, anger, and fear to strangers simply by watching them move, even when only point-light displays of the moving joints are visible. What has changed is the technological capability to detect these signals automatically. Advances in sensing technologies, from markerless depth cameras and motion capture systems to wearable inertial measurement units and even floor vibration sensors, combined with the maturation of deep learning, have transformed gait-based emotion recognition from a curiosity of behavioral science into a viable computational discipline.

The review’s quantitative findings reveal a field now dominated by neural approaches. Deep learning methods appeared in forty-seven percent of the analyzed studies, reflecting the field’s decisive shift away from handcrafted features toward representations learned directly from data. Hybrid models, which combine deep learning components with classical machine learning techniques or multiple network architectures, accounted for thirty-one percent of the articles. Purely classical machine learning approaches, once the backbone of the field, now appear in just twenty-two percent of studies. This distribution mirrors broader trends in computer vision and affective computing, where end-to-end learned pipelines have consistently outperformed manually engineered feature descriptors such as gait energy images, joint angle statistics, or discrete cosine transform coefficients.

Perhaps the most striking technical finding concerns which architectures perform best. Graph convolutional neural networks achieved the highest average recognition accuracy at 89.50 percent, followed closely by spatio-temporal graph convolutional networks at 88 percent. The success of these graph-based models is no accident. Human walking is inherently a structured, relational phenomenon: joints are connected by bones, and their trajectories evolve over time. Graph convolutional networks represent the human skeleton as a graph of nodes and edges, allowing the network to learn how local joint configurations and global body posture jointly encode emotion. Spatio-temporal variants extend this idea by adding temporal edges that connect the same joint across consecutive frames, enabling the model to capture both the static shape of the body and the dynamic flow of movement, precisely the two information channels that psychophysics experiments have identified as critical for human perception of emotion from gait.

The review also documents a clear hierarchy among benchmark datasets. The Emotion-Gait dataset dominated the literature, appearing in 24.10 percent of the analyzed studies. The Carnegie Mellon University Motion Capture Database, known as CMU-MoCap, and the Edinburgh Locomotion Mocap Dataset, often paired with a deception detection framework abbreviated as DFD, followed with 12.20 percent each. This concentration highlights both a strength and a vulnerability of the field. Standardized benchmarks enable fair comparison between methods, but heavy reliance on a small number of datasets, many of which contain acted or posed emotional walks rather than naturally occurring emotions, raises questions about how well laboratory performance will translate to real-world deployment. The authors note that validation practices across the field remain uneven, with considerable variation in cross-validation schemes, evaluation metrics, and participant demographics.

The methodological pipeline described across the reviewed studies typically unfolds in several stages. Data is first acquired through one of several modalities: optical motion capture systems providing three-dimensional joint coordinates, consumer depth sensors such as the Microsoft Kinect, wearable accelerometers and gyroscopes mounted on the body or embedded in smartphones and smartwatches, or plantar pressure and floor vibration platforms. Preprocessing then segments individual gait cycles and normalizes for walking speed and body proportions. Feature extraction follows, ranging from kinematic descriptors such as stride length, cadence, joint angles, and movement smoothness, to learned representations produced by convolutional or recurrent layers. Finally, classification maps these features onto emotional categories, most commonly discrete labels drawn from Ekman’s basic emotions or dimensional representations along axes of arousal, valence, and dominance.

Beyond the raw accuracy figures, the review situates gait-based emotion recognition within a wider multimodal ecosystem. Emotion is expressed through faces, voices, physiological signals, and body movement simultaneously, and the most robust systems increasingly fuse information across these channels. The reviewed literature includes hybrid frameworks that combine gait data with electroencephalography, speech, facial expressions, and contextual information. Multimodal fusion offers redundancy and resilience, allowing systems to maintain performance when any single channel is degraded. At the same time, the review points to persistent challenges: small and demographically narrow datasets, difficulty distinguishing felt emotions from merely expressed ones, sensitivity to clothing, viewing angle, and environmental conditions, and the computational demands of real-time processing on mobile and embedded platforms.

The application landscape emerging from the review is strikingly broad. In healthcare, gait emotion analysis shows promise for monitoring depression, anxiety, and mood disorders, offering an unobtrusive complement to subjective clinical assessments; several reviewed studies demonstrated that depression risk could be detected from gait data alone, and that smartwatch signals could support emotion recognition in Parkinson’s disease patients. In human-robot interaction, emotionally aware robots could navigate crowds more safely by anticipating the affective states of pedestrians, adjusting their behavior to the perceived mood of the people around them. Smart homes and assistive technologies could adapt lighting, music, or alerts to a resident’s emotional state inferred from their natural walking. Automotive applications include driver state monitoring through wearable sensors, while security and forensic domains have explored emotional gait cues for investigative purposes.

The review does not shy away from the ethical dimensions of this technology. The authors and the broader literature they survey acknowledge that continuous, passive emotion monitoring raises substantial privacy concerns. Unlike facial recognition, gait can be captured at a distance, without the subject’s awareness or cooperation, and even through clothing-invariant, privacy-preserving pipelines. Several researchers have accordingly explored architectures designed specifically to decouple identity information from emotional content, so that systems can recognize affect without uniquely identifying individuals. Regulatory frameworks for emotional surveillance remain immature, and the review implicitly underscores the need for careful governance as these systems approach commercial deployment, particularly in workplaces, public spaces, and healthcare settings where consent dynamics are asymmetric.

Looking forward, the authors identify several research frontiers. Larger, more diverse, and more ecologically valid datasets are urgently needed, ideally capturing spontaneous rather than performed emotion in real-world environments. Self-supervised and transfer learning approaches could reduce dependence on labeled data, and recent work on learning gait emotion representations from unlabeled skeleton sequences points in this direction. Transformer-based architectures, already displacing convolutional models in many vision tasks, are beginning to appear in gait emotion recognition and may push accuracies beyond the current ceiling near ninety percent. Explainability also demands attention: clinicians and end users will need to understand which kinematic cues drive a system’s judgment of sadness or anger before trusting it in consequential decisions.

For a field that began with psychologists noting that sad people walk differently, the trajectory is remarkable. What this systematic review makes clear is that the machine perception of emotion from movement has crossed a threshold of maturity. With graph-based deep architectures consistently achieving near-human recognition rates, standardized benchmarks in place, and application domains from mental health monitoring to socially aware robotics actively under development, gait-based emotion recognition is poised to move from the laboratory into everyday ambient intelligence. The challenges that remain, in data, validation, and ethics, are substantial, but the review provides the field with something it has lacked: a rigorous, transparent map of where it stands and where it must go next.