Free Walking Research Articles

Right visual field advantage in orientation discrimination is influenced by biased suppression

Visual input is not equally processed over space. In recent years, a right visual field advantage during free walking and standing in orientation discrimination and contrast detection task was reported. The current study investigated the underlying mechanism of the previously reported right visual field advantage. It particularly tested if the advantage is driven by a stronger suppression of distracting input from the left visual field or improved processing of targets from the right visual field. Combing behavioural and electrophysiological measurements in a mobile EEG and augmented reality setup, human participants (n = 30) in a standing and a walking condition performed a line orientation discrimination task with stimulus eccentricity and distractor status being manipulated. The right visual field advantage, as demonstrated in accuracy and reaction time, was influenced by the distractor status. Specifically, the right visual field advantage was only observed when the target had an incongruent line orientation with the distractor. Neural data further showed that the right visual field advantage was paralleled by a strong modulation of neural activity in the right hemisphere (i.e. contralateral to the distractor). A significant positive correlation between this right hemispheric event related potential (ERP) and behavioural measures (accuracy and reaction time) was found exclusively for trials in which a target was presented on the right and an incongruent distractor was presented on the left. The right hemispheric ERP component further predicted the strength of the right visual field advantage. Notably, the lateralised brain activity and the right visual field advantage were both independent of stimulus eccentricity and the movement state of participants. Overall, our findings suggest an important role of spatially biased suppression of left distracting input in the right visual field advantage as found in orientation discrimination.

Adult Herbst-multibracket appliance treatment-how stable are the results very long term?

To investigate the post-treatment (Tx) changes (≥ 15 years) in adult Class II patients treated with Herbst appliance and subsequently a multibracket appliance (MBA). Of 51 potential patients 31 could be located and participated (19 II:1 and 12 II:2). At a mean age of 21.8 ± 7.3 years (T0), Herbst-MBA Tx had been started. The mean post-Tx observation period was 19.8 ± 3.6 years. Study models from before and after Tx (T1, mean age: 23.6 ± 7.4 years), after 3.0 ± 2.8 years of retention (T2, mean age: 26.8 ± 8.3 years) as well as after recall (T3, mean age: 43.4 ± 7.5 years) were evaluated using the PAR index as well as standard occlusal variables. Pre-Tx, the mean values of the 31 participants were: PAR score = 26.1 ± 9.6 points, Class II molar relationship (MR) = 0.7 ± 0.2 cusp widths (CW), overjet = 6.9 ± 2.3mm and overbite = 4.8 ± 2.6mm. After Tx, the mean PAR score was 5.3 ± 2.8. A Class I MR (0.0 ± 0.1 CW) was present while overjet and overbite had decreased to 2.1 ± 0.7 and 1.3 ± 0.8mm, respectively. At recall (19.8 ± 3.6 years post-Tx), a slight PAR score increase (+ 3.1 points) had occurred (final value: 8.4 ± 3.7); this was mainly due to mild increases in overjet, overbite (final values: 3.3 ± 1.4mm and 2.5 ± 1.7mm) and changes in sagittal MR (0.2 ± 0.2 CW). The sample size and the participation rate as well as the unavailability of a comparable control group can be considered as limitations. In addition, no long-term radiographic data could be obtained, and the retention protocol was not uniform. The majority of these issues, however, should be acceptable to due to the duration of the observation period and the uniqueness of the data. In the present sample, Herbst-MBA Tx enabled Class II correction in adults with very good occlusal long-term stability.

Analysis of Gait Kinematics in Smart Walker-Assisted Locomotion in Immersive Virtual Reality Scenario.

The decline in neuromusculoskeletal capabilities of older adults can affect motor control, independence, and locomotion. Because the elderly population is increasing worldwide, assisting independent mobility and improving rehabilitation therapies has become a priority. The combination of rehabilitation robotic devices and virtual reality (VR) tools can be used in gait training to improve clinical outcomes, motivation, and treatment adherence. Nevertheless, VR tools may be associated with cybersickness and changes in gait kinematics. This paper analyzes the gait parameters of fourteen elderly participants across three experimental tasks: free walking (FW), smart walker-assisted gait (AW), and smart walker-assisted gait combined with VR assistance (VRAW). The kinematic parameters of both lower limbs were captured by a 3D wearable motion capture system. This research aims at assessing the kinematic adaptations when using a smart walker and how the integration between this robotic device and the VR tool can influence such adaptations. Additionally, cybersickness symptoms were investigated using a questionnaire for virtual rehabilitation systems after the VRAW task. The experimental data indicate significant differences between FW and both AW and VRAW. Specifically, there was an overall reduction in sagittal motion of 16%, 25%, and 38% in the hip, knee, and ankle, respectively, for both AW and VRAW compared to FW. However, no significant differences between the AW and VRAW kinematic parameters and no adverse symptoms related to VR were identified. These results indicate that VR technology can be used in walker-assisted gait rehabilitation without compromising kinematic performance and presenting potential benefits related to motivation and treatment adherence.

Effects of interval training in cardiovascular disease populations: a clinical perspective.

Nearly 20 years have elapsed since the first clinical trial investigated the impact of interval training on patients with cardiovascular disease (CVD). This clinical corner discusses the health outcomes of systematic reviews and meta-analyses and appropriately powered randomized clinical trials which have tested these interval training programs across various CVDs (i.e., coronary artery disease, heart failure, atrial fibrillation, peripheral arterial disease, and cardiac implantable electronic devices). The publications included in this clinical corner have shown that interval training leads to similar or superior improvements in peak oxygen uptake (V̇O2peak), functional capacity, pain free walking, quality of life, anxiety, depression, and endothelial function, but the magnitude of improvements across varying protocols (e.g., length and number of work periods, intensities of work periods, duration of exercise sessions, frequency of exercise sessions, and duration of program) and optimal dosage for males and females are unclear across CVD conditions. The heterogeneity in protocols, physical and mental health outcome measures, and lack of sex- and gender-based analyses calls for more high-quality research in this area.

Ensemble of HMMs for Sequence Prediction on Multivariate Biomedical Data

Background: Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for the description and modeling of disease progression. Deciphering potential underlying unknowns from the distinct observation would substantially improve the understanding of pathological cascades. Hidden Markov Models (HMMs) have been successfully applied to the processing of possibly noisy continuous signals. We apply ensembles of HMMs to categorically distributed multivariate time series data, leaving space for expert domain knowledge in the prediction process. Methods: We use an ensemble of HMMs to predict the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. Results: We present a prediction pipeline that processes data paired with a configuration file, enabling us to train, validate and query an ensemble of HMMs. In particular, we provide a theoretical and practical framework for multivariate time-series inference based on HMMs that includes constructing multiple HMMs, each to predict a particular observable variable. Our analysis is conducted on pseudo-data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. Conclusions: We find that the model shows promising results for the data we tested. The strength of this approach is that HMMs are well understood, probabilistic and interpretable models, setting it apart from most Deep Learning approaches. We publish all code and evaluation pseudo-data in an open-source repository.

Beta Burst-Driven Adaptive Deep Brain Stimulation Improves Gait Impairment and Freezing of Gait in Parkinson's Disease.

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) that is often refractory to medication. Pathological prolonged beta bursts within the subthalamic nucleus (STN) are associated with both worse impairment and freezing behavior in PD, which are improved with deep brain stimulation (DBS). The goal of the current study was to investigate the feasibility, safety, and tolerability of beta burst-driven adaptive DBS (aDBS) for FOG in PD. Seven individuals with PD were implanted with the investigational Summit™ RC+S DBS system (Medtronic, PLC) with leads placed bilaterally in the STN. A PC-in-the-loop architecture was used to adjust stimulation amplitude in real-time based on the observed beta burst durations in the STN. Participants performed either a harnessed stepping-in-place task or a free walking turning and barrier course, as well as clinical motor assessments and instrumented measures of bradykinesia, OFF stimulation, on aDBS, continuous DBS (cDBS), or random intermittent DBS (iDBS). Beta burst driven aDBS was successfully implemented and deemed safe and tolerable in all seven participants. Gait metrics such as overall percent time freezing and mean peak shank angular velocity improved from OFF to aDBS and showed similar efficacy as cDBS. Similar improvements were also seen for overall clinical motor impairment, including tremor, as well as quantitative metrics of bradykinesia. Beta burst driven adaptive DBS was feasible, safe, and tolerable in individuals with PD with gait impairment and FOG.

A biomechanics and energetics dataset of neurotypical adults walking with and without kinematic constraints

Numerous studies have explored the biomechanics and energetics of human walking, offering valuable insights into how we walk. However, prior studies focused on changing external factors (e.g., walking speed) and examined group averages and trends rather than individual adaptations in the presence of internal constraints (e.g., injury-related muscle weakness). To address this gap, this paper presents an open dataset of human walking biomechanics and energetics collected from 21 neurotypical young adults. To investigate the effects of internal constraints (reduced joint range of motion), the participants are both the control group (free walking) and the intervention group (constrained walking - left knee fully extended using a passive orthosis). Each subject walked on a dual-belt treadmill at three speeds (0.4, 0.8, and 1.1 m/s) and five step frequencies ( − 10% to 20% of their preferred frequency) for a total of 30 test conditions. The dataset includes raw and segmented data featuring ground reaction forces, joint motion, muscle activity, and metabolic data. Additionally, a sample code is provided for basic data manipulation and visualisation.

Digital Gait Outcomes for Autosomal Recessive Spastic Ataxia of Charlevoix‐Saguenay (ARSACS): Discriminative, Convergent, and Ecological Validity in a Multicenter Study (PROSPAX)

AbstractBackgroundWith treatment trials on the horizon, this study aimed to identify candidate digital‐motor gait outcomes for autosomal recessive spastic ataxia of Charlevoix‐Saguenay (ARSACS), capturable by wearable sensors with multicenter validity, and ideally also ecological validity during free walking outside laboratory settings.MethodsCross‐sectional multicenter study (four centers), with gait assessments in 36 subjects (18 ARSACS patients; 18 controls) using three body‐worn sensors (Opal, APDM) in laboratory settings and free walking in public spaces. Sensor gait measures were analyzed for discriminative validity from controls, and for convergent (ie, clinical and patient relevance) validity by correlations with SPRSmobility (primary outcome) and Scale for the Assessment and Rating of Ataxia (SARA), Spastic Paraplegia Rating Scale (SPRS), and activities of daily living subscore of the Friedreich Ataxia Rating Scale (FARS‐ADL) (exploratory outcomes).ResultsOf 30 hypothesis‐based digital gait measures, 14 measures discriminated ARSACS patients from controls with large effect sizes (|Cliff's δ| > 0.8) in laboratory settings, with strongest discrimination by measures of spatiotemporal variability Lateral Step Deviation (δ = 0.98), SPcmp (δ = 0.94), and Swing CV (δ = 0.93). Large correlations with the SPRSmobility were observed for Swing CV (Spearman's ρ = 0.84), Speed (ρ = −0.63), and Harmonic Ratio V (ρ = −0.62). During supervised free walking in a public space, 11/30 gait measures discriminated ARSACS from controls with large effect sizes. Large correlations with SPRSmobility were here observed for Swing CV (ρ = 0.78) and Speed (ρ = −0.69), without reductions in effect sizes compared with laboratory settings.ConclusionsWe identified a promising set of digital‐motor candidate gait outcomes for ARSACS, applicable in multicenter settings, correlating with patient‐relevant health aspects, and with high validity also outside laboratory settings, thus simulating real‐life walking with higher ecological validity. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Predictive value of anthropometric measurements in survival and free walking ability of geriatric hip fractures after surgery.

We aimed to explore the predictive value of anthropometric measurements in survival and free walking ability of geriatric hip fractures after surgery. Eight common anthropometric measurements, including arm circumference (AC), waist circumference (WC), thigh circumference (TC), calf circumference (CC), biceps skinfold (BS), triceps skinfold (TS), suprailiac skinfold (SIS), and subscapular skinfold (SSS), were included to identify their predictive value in survival and free walking ability of geriatric hip fractures. The results of anthropometric measurements were compared between patients with different outcomes. Cox and logistics models were established to further identify the predictive value of anthropometric measurements. Comparison among groups indicated that individuals with different outcomes may have significantly different anthropometric measurements. In the Cox analyses based on all individuals, all models proved that the patients with higher AC, as well as CC and BS, may have a lower risk of 1-year mortality. Similarly, in the logistics analysis, AC, CC, and BS were proven to have strong predictive ability for 6-month and 1-year mortality in females and overall individuals. However, the predictive value of the eight common anthropometric measurements in free walking ability is not significant. AC, CC, and BS may have strong predictive ability for 6-month and 1-year mortality in all individuals and females.

Nordic walking and arm swing asymmetry in people with Parkinson’s disease: protocol for a randomised clinical trial

People with Parkinson’s disease (PD) face disruptions in arm swing (AS) motion during walking, including a reduction in amplitude and an increase in asymmetry. Both conditions are detrimental to gait...

A study of cognitive impairment quantitative assessment method based on gait characteristics

Alzheimer's disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject's MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.

Comparative Analysis of Gait Kinematics in Older Adults: Free Walking vs. Nordic Walking with Classic and Mechatronic Poles

This investigation aimed to scrutinise the kinematic and spatio-temporal characteristics of free and Nordic walking (NW) in older adults, utilising both traditional and biosensor-integrated mechatronic poles. The hypothesis was that including biosensors for gait analysis in NW poles would not disrupt the walking pattern of older adults compared to standard poles. The study involved seniors aged 65–74 who were amateur Nordic Walking (NW) practitioners. They were instructed to traverse a 100-m distance three times: without poles, with traditional NW poles, and with mechatronic poles. The MyoMOTION MR3 system was used to gather biomechanical data. The study revealed differences in eight kinematic parameters and stride time between free walking and walking with poles. However, no statistically significant differences were observed in any of the parameters, regardless of the pole type used. The findings suggest that older NW practitioners tend to employ free walking patterns even when using poles.

Functional electrostimulation therapy for vastus medialis decreases the varus thrust during gait.

[Purpose] This study aimed to investigate whether modification of vastus medialis activity can delay the varus thrust. [Participants and Methods] Ten participants (Kellgren-Laurence grades I: n=2, II: n=6, and III: n=2) diagnosed with knee osteoarthritis were enrolled. The intervention involved free walking on a 10-m walkway at any speed after donning a functional electrical stimulation set to contract the vastus medialis before heel contact. Using a Vicon Nexus ground reaction force meter and a wireless electromyograph DELSYS, varus thrust, maximal knee extension angle, maximal knee adduction moment, and vastus medialis onset time were assessed both before and after intervention. [Results] A significant difference in varus thrust was detected from before to after the intervention (2.7 ± 1.1° vs. 2.2 ± 1.3°). Both the vastus medialis activation time (-0.06 ± 0.09 vs. -0.21 ± 0.1) and the knee-joint extension angle (8.7 ± 5.1° vs. 5.5 ± 5.9°) decreased following intervention, whereas the knee adduction moment significantly increased (0.50 ± 0.20° vs 0.56 ± 0.18°). [Conclusion] Wearing the functional electrical stimulation set caused the vastus medialis to act earlier in response to heel strike, thereby improving the knee-joint extension angle and suppressing varus thrust.

Mandibular second molar extraction: A retrospective cohort study of spontaneous occlusal changes in adolescent patients.

Evaluate long-term spontaneous occlusal changes following L7 extraction in adolescent patients. Study models of 144 participants (63 males, 81 females) retrospectively assessed prior to L7 extraction (9-16 years old; T1) and following L8 eruption (14-25 years old; T2). All received upper fixed appliances. A sub-group (n = 86) received no lower fixed appliances and acted as controls. Occlusal changes were compared between treatment (lower fixed appliance) and control (no lower fixed appliance) groups using PAR index. At T2, L8 occlusal outcome was assessed using ABO grading system. Mean follow-up period 6 (SD 2) years. At T1, lower scores observed in control group for Lower Anterior (P < .001), Midline (P = .033) and Lateral Segments (P = .040) components. At T2, lower scores continued being observed in control group for Midline (P < .001) and Lateral segment (P = .019) components. Higher decrease in Lower Anterior PAR scores observed in treatment group (<.001) with comparable scores between groups at T2 (P = .057). Similar PAR score changes between groups for Lateral Segments, Overjet and Overbite components. At T2, no significant difference observed in Total PAR score reduction between control (83%) and treatment (82%) groups. Good-to-acceptable occlusal outcome of the L8 observed in 81.55% of cases at T2 with no difference between groups. In growing patients with mild mandibular crowding, extraction of L7 followed by upper fixed appliance therapy, leads to favourable occlusal changes over a 6-year follow-up period, with or without lower fixed appliance therapy, being an alternative extraction protocol where lower fixed appliance therapy is not recommended.

Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach.

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant's level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body's center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual's stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Optimizing Exoskeleton Assistance: Muscle Synergy-Based Actuation for Personalized Hip Exoskeleton Control

Exoskeleton robots hold promising prospects for rehabilitation training in individuals with weakened muscular conditions. However, achieving improved human–machine interaction and delivering customized assistance remains a challenging task. This paper introduces a muscle synergy-based human-in-the-loop (HIL) optimization framework for hip exoskeletons to offer more personalized torque assistance. Initially, we propose a muscle synergy similarity index to quantify the similarity of synergy while walking with and without the assistance of an exoskeleton. By integrating surface electromyography (sEMG) signals to calculate metrics evaluating muscle synergy and iteratively optimizing assistance parameters in real time, a muscle synergy-based HIL optimized torque configuration is presented and tested on a portable hip exoskeleton. Iterative optimization explores the optimal and suboptimal assistance torque profiles for six healthy volunteers, simultaneously testing zero torque and predefined assistance configurations, and verified the corresponding muscle synergy similarity indices through experimental testing. In our validation experiments, the assistance parameters generated through HIL optimization significantly enhance muscle synergy similarity during walking with exoskeletal assistance, with an optimal average of 0.80 ± 0.04 (mean ± std), marking a 6.3% improvement over prior assistive studies and achieving 96.4% similarity compared with free walking. This demonstrates that the proposed muscle synergy-based HIL optimization can ensure robotic exoskeleton-assisted walking as “natural” as possible.

Real-walk modelling: deep learning model for user mobility in virtual reality

This paper presents a study on modelling user free walk mobility in virtual reality (VR) art exhibition. The main objective is to investigate and model users’ mobility sequences during interactions with artwork in VR. We employ a range of machine learning (ML) techniques to define scenes of interest in VR, capturing user mobility patterns. Our approach utilises a long short-term memory (LSTM) model to effectively model and predict users’ future movements in VR environments, particularly in scenarios where clear walking paths and directions are not provided to participants. The DL model demonstrates high accuracy in predicting user movements, enabling a better understanding of audience interactions with the artwork. It opens avenues for developing new VR applications, such as community-based navigation, virtual art guides, and enhanced virtual audience engagement. The results highlight the potential for improved user engagement and effective navigation within virtual environments.

The nature of gait biomechanics changes with walking speed increase in patients with anterior cruciate ligament injury.

This study on gait biomechanics is based on a functional test (FT) performed at free and fast walking speeds. We investigated the pattern of changes in gait biomechanical parameters and the knee function in patients after anterior cruciate ligament (ACL) injury or its reconstruction. The study included 51 patients (33 males, 18 females) with a confirmed recent or old history of ACL tear, before or after reconstruction (26 patients). Mocap data was obtained using an inertial system. All patients were divided into three groups: 41 patients with physiological response (compensation), 6 patients with signs of decompensation, and 4 non-assessable patients. Increase in gait speed was associated with decrease in the walking cycle duration, stance and double support phases. In the compensation group, the physiological response of the knee was manifested in increased amplitudes. In the decompensation group, the amplitudes remained unchanged or decreased. In the compensation group, there were increases in the symmetrical muscle activity. The decompensation group was characterized by electromyography asymmetry. The observed universal physiological mechanism limiting the load on the damaged joint can be used for accurate assessment of the knee functional state in various periods of rehabilitation.

Evaluation of efficacy, treatment outcome, and stability of tooth movement with clear aligner treatment (Mysmartalign) - Clinical study

The study was conducted to evaluate the efficacy, treatment outcomes, and stability of tooth movement with clear aligner therapy. Fifty subjects were selected for aligner treatment and followed a retention protocol as instructed by the orthodontist. To calculate efficacy, changes in Little's Irregularity Index from T0 to T4, T0 to T6, and T0 to T8 of clinical plaster models were compared with STL models for both the maxilla and mandible. A comparison of pre-treatment and posttreatment PAR scores was performed to assess treatment outcomes. Stability was evaluated using the PAR score by comparing post-treatment and one-year follow-up study casts. An independent t-test was used to compare the mean values acquired from the same sample. The mean change in Little's Irregularity Index from T0 to T4, T0 to T6, and T0 to T8 of clinical plaster models was compared with software STL models for both the maxilla and mandible. It was observed that the mean change was greater in the clinical models at each stage. Treatment outcomes showed significant improvement for 16 subjects (32%), while 34 subjects (68%) also showed improvement. A paired t-test was conducted to assess stability between post-treatment and the one-year follow-up, and the results were not statistically significant. The software models overestimated alignment and the resolution of crowding when compared to the actual clinical plaster models. Consequently, the PAR score assessment indicated successful treatment outcomes. However, regarding stability, mild relapses were observed after the one-year follow-up.