Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

Actionable Genotypes and Their Association with Life Span in Iceland

Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available.Evaluations of the association of actionable genotypes in these genes with life span are currently lacking.Methods:We assessed the prevalence of coding and splice variants in genes on the ACMG Secondary Findings,version 3.0(ACMG SF v3.0),list in the genomes of 57,933 Icelanders.We assigned pathogenicity to all reviewed variants using reported evidence in the ClinVar database,the frequency of variants,and their associations with disease to create a manually curated set of actionable genotypes(variants).We assessed the relationship between these genotypes and life span and further examined the specific causes of death among carriers.

基于LDA主题模型的GitHub Actions工作流项目推荐算法

在CI/CD实践中,自动化已成为软件开发实践中的一种规范。GitHub引入GitHub Actions为软件维护者提供自动化的持续集成工作流方案,尽管其为开发者提供了诸多便利,GitHub社区也提供了许多第三方的GitHub Actions服务,但仍然只有极少的项目在使用。为了满足开发人员对工作流自动化的需求,减少非开发任务工作量,提出一种基于隐含狄利克雷分布(LDA)主题模型和Jensen-Shannon距离的GitHub Actions工作流项目推荐算法。通过对GitHub Actions存储库的README文件进行主题建模,将GitHub的事件描述文本和用户输入作为模型输入,为正在开发的代码仓库推荐GitHub Actions服务。将该推荐模型与标准的基于余弦相似度的方法进行比较的实验结果表明,该方法能有效改善开源软件仓库的推荐精度。

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

Unveiling health rights:A call to action for sex workers'HIV care in the Philippines

We are writing in response to the article titled“Addressing the needs and rights of sex workers for HIV healthcare services in the Philippines”[1].The article calls for attention on the significant challenges faced by sex workers in the Philippines in accessing HIV healthcare.We appreciate the article’s effort to examine these issues in depth.We would like to present a constant flow of thoughts in this letter while highlighting the positive aspects,potential obstacles,and additional points that contribute to this ongoing discussion.

General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

Adjustment mechanism of blasting dynamic-static action in the water decoupling charge

Water decoupling charge blasting excels in rock breaking,relying on its uniform pressure transmission and low energy dissipation.The water decoupling coefficients can adjust the contributions of the stress wave and quasi-static pressure.However,the quantitative relationship between the two contributions is unclear,and it is difficult to provide reasonable theoretical support for the design of water decoupling blasting.In this study,a theoretical model of blasting fracturing partitioning is established.The mechanical mechanism and determination method of the optimal decoupling coefficient are obtained.The reliability is verified through model experiments and a field test.The results show that with the increasing of decoupling coefficient,the rock breaking ability of blasting dynamic action decreases,while quasi-static action increases and then decreases.The ability of quasi-static action to wedge into cracks changes due to the spatial adjustment of the blast hole and crushed zone.The quasi-static action plays a leading role in the fracturing range,determining an optimal decoupling coefficient.The optimal water decoupling coefficient is not a fixed value,which can be obtained by the proposed theoretical model.Compared with the theoretical results,the maximum error in the model experiment results is 8.03%,and the error in the field test result is 3.04%.

Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios

Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training module.These components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training efficiency.The TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant information.The prefix training module facilitates efficient fine-tuning and knowledge transfer.Experimental results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR method.This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies.The proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety.

Developments in the study of Chinese herbal medicine's assessment index and action mechanism for diabetes mellitus

In traditional Chinese medicine(TCM),based on various pathogenic symptoms and the‘golden chamber’medical text,Huangdi Neijing,diabetes mellitus falls under the category‘collateral disease’.TCM,with its wealth of experience,has been treating diabetes for over two millennia.Different antidiabetic Chinese herbal medicines re-duce blood sugar,with their effective ingredients exerting unique advantages.As well as a glucose lowering effect,TCM also regulates bodily functions to prevent diabetes associated complications,with reduced side effects compared to western synthetic drugs.Chinese herbal medicine is usually composed of polysaccharides,saponins,al-kaloids,flavonoids,and terpenoids.These active ingredients reduce blood sugar via various mechanism of actions that include boosting endogenous insulin secretion,enhancing insulin sensitivity and adjusting key enzyme activity and scavenging free radicals.These actions regulate glycolipid metabolism in the body,eventually achiev-ing the goal of normalizing blood glucose.Using different animal models,a number of molecular markers are available for the detection of diabetes induction and the molecular pathology of the disease is becoming clearer.Nonetheless,there is a dearth of scientific data about the pharmacology,dose-effect relationship,and structure-activity relationship of TCM and its constituents.Further research into the efficacy,toxicity and mode of action of TCM,using different metabolic and molecular markers,is key to developing novel TCM antidiabetic formulations.

HgaNets:Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.

Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model

Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns.Deep learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like badminton.We proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action recognition.The data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal data.The three-dimensional distance between each skeleton point and the right hip represents the spatial features.The temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video sequence.The weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action recognition.The E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.

Experimental study on the movement of oil spill under freeze-thaw action

Oil leakages cause environmental pollution,economic losses,and even engineering safety accidents.In cold regions,researchers urgently investigate the movement of oil spill in soils exposed to freeze-thaw cycles.In this study,a series of laboratory model experiments were carried out on the migration of oil leakage under freeze-thaw action,and the distributions of the soil temperature,unfrozen water content,and displacement were analyzed.The results showed that under freeze-thaw action,liquid water in soils migrated to the freezing front and accumulated.After the pipe cracked,oil pollutants first gathered at one side of the leak hole,and then moved around.The pipe wall temperature affected the soil temperature field,and the thermal influence range below and transverse the pipe wall(35–40 cm)was larger than that above the pipe wall(8 cm)owing to the soil surface temperature.The leaked oil's temperature would make the temperature of the surrounding soil rise.Oil would inhibit the cooling of the soils.Besides,oil migration was significantly affected by the gravity and water flow patterns.The freeze-thaw action would affect the migration of the oil,which was mainly manifested as inhibiting the diffusion and movement of oil when soils were frozen.Unfrozen water transport caused by freeze-thaw cycles would also inhibit oil migration.The research results would provide a scientific reference for understanding the relationship between the movement of oil pollutants,water,and soil temperature,and for establishing a waterheat-mass transport model in frozen soils.

Design,preparation,application of advanced array structured materials and their action mechanism analyses for high performance lithium-sulfur batteries

Lithium-sulfur battery(LSB)has brought much attention and concern because of high theoretical specific capacity and energy density as one of main competitors for next-generation energy storage systems.The widely commercial application and development of LSB is mainly hindered by serious“shuttle effect”of lithium polysulfides(Li PSs),slow reaction kinetics,notorious lithium dendrites,etc.In various structures of LSB materials,array structured materials,possessing the composition of ordered micro units with the same or similar characteristics of each unit,present excellent application potential for various secondary cells due to some merits such as immobilization of active substances,high specific surface area,appropriate pore sizes,easy modification of functional material surface,accommodated huge volume change,enough facilitated transportation for electrons/lithium ions,and special functional groups strongly adsorbing Li PSs.Thus many novel array structured materials are applied to battery for tackling thorny problems mentioned above.In this review,recent progresses and developments on array structured materials applied in LSBs including preparation ways,collaborative structural designs based on array structures,and action mechanism analyses in improving electrochemical performance and safety are summarized.Meanwhile,we also have detailed discussion for array structured materials in LSBs and constructed the structure-function relationships between array structured materials and battery performances.Lastly,some directions and prospects about preparation ways,functional modifications,and practical applications of array structured materials in LSBs are generalized.We hope the review can attract more researchers'attention and bring more studying on array structured materials for other secondary batteries including LSB.

Unstable evolution of railway slope under the rainfall-vibration joint action

Understanding the unstable evolution of railway slopes is the premise for preventing slope failure and ensuring the safe operation of trains.However,as two major factors affecting the stability of railway slopes,few scholars have explored the unstable evolution of railway slopes under the joint action of rainfall-vibration.Based on the model test of sandy soil slope,the unstable evolution process of slope under locomotive vibration,rainfall,and rainfall-vibration joint action conditions was simulated in this paper.By comparing and analyzing the variation trends of soil pressure and water content of slope under these conditions,the change laws of pore pressure under the influence of vibration and rainfall were explored.The main control factors affecting the stability of slope structure under the joint action conditions were further defined.Combined with the slope failure phenomena under these three conditions,the causes of slope instability resulting from each leading factor were clarified.Finally,according to the above conclusions,the unstable evolution of the slope under the rainfall-vibration joint action was determined.The test results show that the unstable evolution process of sandy soil slope,under the rainfall-vibration joint action,can be divided into:rainfall erosion cracking,vibration promotion penetrating,and slope instability sliding three stages.In the process of slope unstable evolution,rainfall and vibration play the roles of inducing and promoting slide respectively.In addition,the deep cracks,which are the premise for the formation of the sliding surface,and the violent irregular fluctuation of soil pressure,which reflects the near penetration of the sliding surface,constitute the instability characteristics of the railway slope together.This paper reveals the unstable evolution of sandy soil slopes under the joint action of rainfall-vibration,hoping to provide the theoretical basis for the early warning and prevention technology of railway slopes.

Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

The Effect of Procrastination on Physical Exercise among College Students—The Chain Effect of Exercise Commitment and Action Control

Background:Exercise procrastination is prevalent among college students,causing decline in physical fitness.It is imperative to investigate the mechanism affecting college students’physical activity behaviors.This study was aimed at investigating the effect of procrastination on college students’physical exercise behavior,and the chain mediation effects of exercise commitment and action control(AC),to provide a theoretical basis for interventions targeting physical exercise behavior among college students.Methods:A questionnaire survey was conducted using convenience sampling.The General Procrastination Scale,Exercise Commitment Scale,Action Control Scale,and Physical Activity Rating Scale-3 questionnaires were used.Participants were 581 college students(age 19.27±0.94 years;243 males and 338 females).Statistical methods of regression analysis and structural equation modeling(SEM)were applied.Results:Procrastination,exercise commitment,and action control were found to be significant predictors of physical exercise behavior.Among these predictors,exercise commitment and action control showed full mediation effects in the relationship between procrastination and physical exercise behavior,and explained 25.48%and 30.77%of the total variance,respectively.The chain mediation effect of exercise commitment-action control was significant,accounting for 22.60%of the total variance,and the total indirect effect was 79.33%.Conclusion:Therefore,higher procrastination was associated with less participation in physical exercise behavior among college students.Improvements in exercise commitment and volitional decision-making ability for physical exercise behavior promoted physical exercise behavior,and increased exercise commitment promoted volitional decision-making ability among the students.The chain reaction effect of exercise commitment and action control also buffered the negative effects of procrastination on physical exercise behavior,thereby increasing physical exercise behavior among college students.

Research Progress on Purification Process, Content Determination and Pharmacological Action of Atractylodin

Atractylodis Rhizoma comes from the dry rhizome of Atractylis lancea or Atractylodes chinensis in the Compositae family,and it is suitable for preventing and treating diseases such as cold,edema,night blindness and rheumatic arthralgia.Atractylodin is the main active component extracted and isolated from Atractylodis Rhizoma.A large number of studies have found that atractylodin has excellent drug activity in improving gastrointestinal emptying,anti-inflammation,inhibiting malignant tumor and reducing blood lipid.In this paper,the purification process and pharmacological activity of Atractylodin were summarized to provide a theoretical basis for basic research,clinical application and further development and utilization of atractylodin.

Wmic-GMTS and Wmic-GMERR criteria for micron-scale crack propagation in red-bed soft rocks under hydraulic action

Micron-scale crack propagation in red-bed soft rocks under hydraulic action is a common cause of engineering disasters due to damage to the hard rockesoft rockewater interface.Previous studies have not provided a theoretical analysis of the length,inclination angle,and propagation angle of micron-scale cracks,nor have they established appropriate criteria to describe the crack propagation process.The propagation mechanism of micron-scale cracks in red-bed soft rocks under hydraulic action is not yet fully understood,which makes it challenging to prevent engineering disasters in these types of rocks.To address this issue,we have used the existing generalized maximum tangential stress(GMTS)and generalized maximum energy release rate(GMERR)criteria as the basis and introduced parameters related to micron-scale crack propagation and water action.The GMTS and GMERR criteria for micronscale crack propagation in red-bed soft rocks under hydraulic action(abbreviated as the Wmic-GMTS and Wmic-GMERR criteria,respectively)were established to evaluate micron-scale crack propagation in redbed soft rocks under hydraulic action.The influence of the parameters was also described.The process of micron-scale crack propagation under hydraulic action was monitored using uniaxial compression tests(UCTs)based on digital image correlation(DIC)technology.The study analyzed the length,propagation and inclination angles,and mechanical parameters of micron-scale crack propagation to confirm the reliability of the established criteria.The findings suggest that the Wmic-GMTS and Wmic-GMERR criteria are effective in describing the micron-scale crack propagation in red-bed soft rocks under hydraulic action.This study discusses the mechanism of micron-scale crack propagation and its effect on engineering disasters under hydraulic action.It covers topics such as the internal-external weakening of nano-scale particles,lateral propagation of micron-scale cracks,weakening of the mechanical properties of millimeter-scale soft rocks,and resulting interface damage at the engineering scale.The study provides a theoretical basis for the mechanism of disasters in red-bed soft-rock engineering under hydraulic action.

Impact of Action Observation Therapy along with Usual Physiotherapy Intervention of Individual with Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairments in the initial stage, which lead to severe cognitive dysfunction in the later stage. Action observation therapy (AOT) is a multisensory cognitive rehabilitation technique where the patient initially observes the actions and then tries to perform. The study aimed to examine the impact of AOT along with usual physiotherapy interventions to reduce depression, improve cognition and balance of a patient with AD. A 67 years old patient with AD was selected for this study because the patient has been suffering from depression, dementia, and physical dysfunction along with some other health conditions like diabetes and hypertension. Before starting intervention, a baseline assessment was done through the Beck Depression Inventory (BDI) tool, the Mini-Cog Scale, and the Berg Balance Scale (BBS). The patient received 12 sessions of AOT along with usual physiotherapy interventions thrice a week for four weeks, which included 45 minutes of each session. After four weeks of intervention, the patient demonstrated significant improvement in depression, cognition, and balance, whereas the BDI score declined from moderate 21/63 to mild 15/63 level of depression. The Mini-Cog score improved from 2/5 to 4/5, and the BBS score increased from 18/56 to 37/56. It is concluded that AOT along with usual physiotherapy intervention helps to reduce depression, improve cognition and balance of people with AD.