Recent findings from this review highlight that motor impairments in ASD are not merely peripheral symptoms but are deeply integrated into the neurodevelopmental trajectory of the disorder. The reviewed literature consistently demonstrates that difficulties in motor planning and execution, particularly in sequencing, timing, balance, and coordination, emerge early and may precede the onset of social and communication difficulties (
5). These findings strengthen the conceptualization of motor dysfunction as a core and early-developing component of ASD rather than a secondary manifestation. Such evidence reinforces the idea that motor abnormalities can act as reliable early biomarkers for ASD detection and intervention, providing a potential pathway for preclinical screening and preventive therapeutic approaches (
23).
Moreover, increasing research attention has been devoted to the objective quantification of these motor characteristics using advanced technological methods (
32). Several studies included in this review have employed systems such as three-dimensional motion capture, inertial measurement units (IMUs), and wearable accelerometer-based sensors to examine micro-level kinematic patterns (
32,
33). These tools have enabled precise detection of gait asymmetry, postural instability, atypical hand trajectories, and increased variability in joint movement among children with ASD (
34). Importantly, such technologies overcome the limitations of subjective observation by providing continuous, high-resolution data that reveal subtle motor irregularities not visible to the naked eye (
1,
34). Complementing these biomechanical methods, EEG-based motor decoding and computational modeling have shed light on atypical neural activity associated with motor preparation, sensory feedback integration, and movement execution (
1,
34). Together, these approaches bridge behavioral and neurophysiological evidence, demonstrating that motor planning deficits in ASD reflect both functional network disorganization and sensorimotor integration anomalies (
17).
Parallel to technological advancements, longitudinal and long-term monitoring studies have provided valuable insight into the predictive value of early motor behavior for later developmental outcomes (
17). For example, the SPARK cohort study revealed that approximately 88% of children with ASD exhibit measurable motor dysfunction, underscoring the necessity of including motor evaluation in early diagnostic assessments (
33). Likewise, comprehensive reviews of early behavioral markers 5 indicate that deviations in gross and fine motor milestones, often observable within the first 12 to 18 months, are significant predictors of subsequent ASD diagnosis (
17). These longitudinal observations demonstrate that the developmental cascade from motor deficits to communication and social difficulties may begin much earlier than previously assumed (
1). Therefore, continuous digital monitoring, using wearable motion sensors or home-based camera tracking systems, represents a promising avenue for early detection and risk stratification of ASD in infants (
34).
By integrating findings from both technological and longitudinal research, the present review identifies a critical gap in the literature: While the qualitative descriptions of motor difficulties in ASD are well-established, the systematic integration of technology-based assessment with developmental follow-up remains scarce (
17,
33). Future research should thus adopt multimodal frameworks that combine kinematic, electrophysiological, and behavioral data across different developmental stages (
32). Such approaches will enable researchers to capture the dynamic evolution of motor skills over time, establish normative growth trajectories, and detect atypical motor signatures predictive of social and cognitive outcomes (
33). Additionally, machine learning and artificial intelligence (AI)-assisted analytics can play a transformative role in identifying latent motor patterns and predicting response to interventions based on individualized motor profiles (
5,
33).
Motion imagery training has also been shown to enhance visual-motor perception in children with ASD, supporting the integration of cognitive-motor approaches in therapy (
35). Beyond assessment, evidence from intervention studies reviewed here indicates that motor-based rehabilitation programs, including occupational therapy, sensorimotor training, and structured physical activity, can significantly enhance both fine and gross motor skills in children with ASD (
30,
35). Improvements in postural control, bilateral coordination, and praxis have been observed following consistent motor training (
36). However, the impact of these interventions on social communication outcomes remains variable, suggesting that future trials should integrate technological feedback systems to personalize motor learning strategies and optimize transfer effects across domains (
36). For instance, wearable biofeedback devices and virtual reality-assisted motor training can provide real-time sensory reinforcement, improving motor learning efficiency and engagement in children with ASD (
32). The role of internal attentional focus in facilitating motor learning may partly explain the differential outcomes observed across intervention programs (
17).
5.1. Conclusions
This review demonstrates that motor impairments in ASD are fundamental components of the condition’s neurodevelopmental profile rather than secondary effects. Difficulties in motor sequencing, timing, and coordination emerge early, often before social and communication deficits, highlighting their potential as early diagnostic markers. The evidence reviewed shows that disruptions in motor planning and execution are consistently linked to atypical neural activation and sensory-motor integration deficits, emphasizing the central role of motor function in ASD development.
Informed by recent technological and longitudinal studies, this review supports the integration of advanced technologies and long-term monitoring in future research. Motion capture, wearable sensors, EEG decoding, and AI-based motion analysis offer precise, objective methods for identifying early motor irregularities, while extended developmental tracking can clarify how these deficits evolve and respond to intervention. Combining these approaches within personalized motor training and rehabilitation programs could improve both motor and functional outcomes, paving the way for earlier, data-driven, and more effective therapeutic strategies for individuals with ASD.