1 1666 127 NEURAL NETWORK-ORIENTED BIG DATA MODEL FOR YOGA MOVEMENT RECOGNITION. THE USE OF COMPUTER VISION FOR TARGET DETECTION AND RECOGNITION HAS BEEN AN INTERESTING AND CHALLENGING AREA OF RESEARCH FOR THE PAST THREE DECADES. PROFESSIONAL ATHLETES AND SPORTS ENTHUSIASTS IN GENERAL CAN BE TRAINED WITH APPROPRIATE SYSTEMS FOR CORRECTIVE TRAINING AND ASSISTIVE TRAINING. SUCH A NEED HAS MOTIVATED RESEARCHERS TO COMBINE ARTIFICIAL INTELLIGENCE WITH THE FIELD OF SPORTS TO CONDUCT RESEARCH. IN THIS PAPER, WE PROPOSE A MASK REGION-CONVOLUTIONAL NEURAL NETWORK (MR-CNN)- BASED METHOD FOR YOGA MOVEMENT RECOGNITION BASED ON THE IMAGE TASK OF YOGA MOVEMENT RECOGNITION. THE IMPROVED MR-CNN MODEL IS BASED ON THE FRAMEWORK AND STRUCTURE OF THE REGION-CONVOLUTIONAL NETWORK, WHICH PROPOSES A CERTAIN NUMBER OF CANDIDATE REGIONS FOR THE IMAGE BY FEATURE EXTRACTION AND CLASSIFIES THEM, THEN OUTPUTS THESE REGIONS AS DETECTED BOUNDING BOXES, AND DOES MASK PREDICTION FOR THE CANDIDATE REGIONS USING SEGMENTATION BRANCHES. THE IMPROVED MR-CNN MODEL USES AN IMPROVED DEEP RESIDUAL NETWORK AS THE BACKBONE NETWORK FOR FEATURE EXTRACTION, BILINEAR INTERPOLATION OF THE EXTRACTED CANDIDATE REGIONS USING REGION OF INTEREST (ROI) ALIGN, FOLLOWED BY TARGET CLASSIFICATION AND DETECTION, AND SEGMENTATION OF THE IMAGE USING THE SEGMENTATION BRANCH. THE MODEL IMPROVES THE CONVOLUTION PART IN THE SEGMENTATION BRANCH BY REPLACING THE ORIGINAL STANDARD CONVOLUTION WITH A DEPTH-SEPARABLE CONVOLUTION TO IMPROVE THE NETWORK EFFICIENCY. EXPERIMENTALLY CONSTRUCTED POLYGON-LABELED DATASETS ARE SIMULATED USING THE ALGORITHM. THE DEEPENING OF THE NETWORK AND THE USE OF DEPTH-SEPARABLE NETWORK IMPROVE THE ACCURACY OF DETECTION WHILE MAINTAINING THE RELIABILITY OF THE NETWORK AND VALIDATE THE EFFECTIVENESS OF THE IMPROVED MR-CNN. 2021 2 1649 27 MULTI-OMICS INTEGRATION AND INTERACTOMICS REVEALS MOLECULAR NETWORKS AND REGULATORS OF THE BENEFICIAL EFFECT OF YOGA AND EXERCISE. BACKGROUND: YOGA IS A MULTIFACETED SPIRITUAL TOOL THAT HELPS IN MAINTAINING HEALTH, PEACE OF MIND, AND POSITIVE THOUGHTS. IN THE CONTEXT OF ASANA, YOGA IS SIMILAR TO PHYSICAL EXERCISE. THIS STUDY AIMS TO CONSTRUCT A MOLECULAR NETWORK TO FIND HUB GENES THAT PLAY IMPORTANT ROLES IN PHYSICAL EXERCISE AND YOGA. METHODOLOGY: WE COMBINED DIFFERENTIALLY EXPRESSED GENES (DEGS) IN YOGA AND EXERCISE USING COMPUTATIONAL BIOINFORMATICS FROM PUBLICLY AVAILABLE GENE EXPRESSION OMNIBUS (GEO) DATASETS AND IDENTIFIED THE CODIFFERENTIALLY EXPRESSED MRNAS WITH GEO2R. THE CO-DEGS WERE DIVIDED INTO FOUR DIFFERENT GROUPS AND EACH GROUP WAS SUBJECTED TO PROTEIN-PROTEIN INTERACTION (PPI) NETWORK, PATHWAYS ANALYSIS, AND GENE ONTOLOGY. RESULTS: OUR STUDY IDENTIFIED IMMUNOLOGICAL MODULATION AS A DOMINANT TARGET OF DIFFERENTIAL EXPRESSION IN YOGA AND EXERCISE. YOGA PREDOMINANTLY MODULATED GENES AFFECTING THE TH1 AND NK CELLS, WHEREAS CYTOKINES, MACROPHAGE ACTIVATION, AND OXIDATIVE STRESS WERE AFFECTED BY EXERCISE. WE ALSO OBSERVED THAT WHILE YOGA REGULATED GENES FOR TWO MAIN PHYSIOLOGICAL FUNCTIONS OF THE BODY, NAMELY CIRCADIAN RHYTHM (BHLHE40) AND IMMUNITY (LBP, T-BOX TRANSCRIPTION FACTOR 21, CEACAM1), EXERCISE-REGULATED GENES INVOLVED IN APOPTOSIS (BAG3, PROTEIN KINASE C ALPHA), ANGIOGENESIS, AND CELLULAR ADHESION (EPH RECEPTOR A1). CONCLUSION: THE DISSIMILARITY IN THE GENETIC EXPRESSION PATTERNS IN YOGA AND EXERCISE HIGHLIGHTS THE DISCRETE EFFECT OF EACH IN BIOLOGICAL SYSTEMS. THE INTEGRATION AND CONVERGENCES OF MULTI-OMICS SIGNALS CAN PROVIDE DEEPER AND COMPREHENSIVE INSIGHTS INTO THE VARIOUS BIOLOGICAL MECHANISMS THROUGH WHICH YOGA AND EXERCISE EXERT THEIR BENEFICIAL EFFECTS AND OPENS UP POTENTIAL NEWER RESEARCH AREAS. 2022 3 64 27 A COMPUTER VISION-BASED YOGA POSE GRADING APPROACH USING CONTRASTIVE SKELETON FEATURE REPRESENTATIONS. THE MAIN OBJECTIVE OF YOGA POSE GRADING IS TO ASSESS THE INPUT YOGA POSE AND COMPARE IT TO A STANDARD POSE IN ORDER TO PROVIDE A QUANTITATIVE EVALUATION AS A GRADE. IN THIS PAPER, A COMPUTER VISION-BASED YOGA POSE GRADING APPROACH IS PROPOSED USING CONTRASTIVE SKELETON FEATURE REPRESENTATIONS. FIRST, THE PROPOSED APPROACH EXTRACTS HUMAN BODY SKELETON KEYPOINTS FROM THE INPUT YOGA POSE IMAGE AND THEN FEEDS THEIR COORDINATES INTO A POSE FEATURE ENCODER, WHICH IS TRAINED USING CONTRASTIVE TRIPLET EXAMPLES; FINALLY, A COMPARISON OF SIMILAR ENCODED POSE FEATURES IS MADE. FURTHERMORE, TO TACKLE THE INHERENT CHALLENGE OF COMPOSING CONTRASTIVE EXAMPLES IN POSE FEATURE ENCODING, THIS PAPER PROPOSES A NEW STRATEGY TO USE BOTH A COARSE TRIPLET EXAMPLE-COMPRISED OF AN ANCHOR, A POSITIVE EXAMPLE FROM THE SAME CATEGORY, AND A NEGATIVE EXAMPLE FROM A DIFFERENT CATEGORY, AND A FINE TRIPLET EXAMPLE-COMPRISED OF AN ANCHOR, A POSITIVE EXAMPLE, AND A NEGATIVE EXAMPLE FROM THE SAME CATEGORY WITH DIFFERENT POSE QUALITIES. EXTENSIVE EXPERIMENTS ARE CONDUCTED USING TWO BENCHMARK DATASETS TO DEMONSTRATE THE SUPERIOR PERFORMANCE OF THE PROPOSED APPROACH. 2021 4 1987 32 SPATIAL-TEMPORAL GRAPH CONVOLUTIONAL FRAMEWORK FOR YOGA ACTION RECOGNITION AND GRADING. THE RAPID DEVELOPMENT OF THE INTERNET HAS CHANGED OUR LIVES. MANY PEOPLE GRADUALLY LIKE ONLINE VIDEO YOGA TEACHING. HOWEVER, YOGA BEGINNERS CANNOT MASTER THE STANDARD YOGA POSES JUST BY LEARNING THROUGH VIDEOS, AND HIGH YOGA POSES CAN BRING GREAT DAMAGE OR EVEN DISABILITY TO THE BODY IF THEY ARE NOT STANDARD. TO ADDRESS THIS PROBLEM, WE PROPOSE A YOGA ACTION RECOGNITION AND GRADING SYSTEM BASED ON SPATIAL-TEMPORAL GRAPH CONVOLUTIONAL NEURAL NETWORK. FIRSTLY, WE CAPTURE YOGA MOVEMENT DATA USING A DEPTH CAMERA. THEN WE LABEL THE YOGA EXERCISE VIDEOS FRAME BY FRAME USING LONG SHORT-TERM MEMORY NETWORK; THEN WE EXTRACT THE SKELETAL JOINT POINT FEATURES SEQUENTIALLY USING GRAPH CONVOLUTION; THEN WE ARRANGE EACH VIDEO FRAME FROM SPATIAL-TEMPORAL DIMENSION AND CORRELATE THE JOINT POINTS IN EACH FRAME AND NEIGHBORING FRAMES WITH SPATIAL-TEMPORAL INFORMATION TO OBTAIN THE CONNECTION BETWEEN JOINTS. FINALLY, THE IDENTIFIED YOGA MOVEMENTS ARE PREDICTED AND GRADED. EXPERIMENT PROVES THAT OUR METHOD CAN ACCURATELY IDENTIFY AND CLASSIFY YOGA POSES; IT ALSO CAN IDENTIFY WHETHER YOGA POSES ARE STANDARD OR NOT AND GIVE FEEDBACK TO YOGIS IN TIME TO PREVENT BODY DAMAGE CAUSED BY NONSTANDARD POSES. 2022 5 612 28 DEVELOPMENT OF A YOGA POSTURE COACHING SYSTEM USING AN INTERACTIVE DISPLAY BASED ON TRANSFER LEARNING. YOGA IS A FORM OF EXERCISE THAT IS BENEFICIAL FOR HEALTH, FOCUSING ON PHYSICAL, MENTAL, AND SPIRITUAL CONNECTIONS. HOWEVER, PRACTICING YOGA AND ADOPTING INCORRECT POSTURES CAN CAUSE HEALTH PROBLEMS, SUCH AS MUSCLE SPRAINS AND PAIN. IN THIS STUDY, WE PROPOSE THE DEVELOPMENT OF A YOGA POSTURE COACHING SYSTEM USING AN INTERACTIVE DISPLAY, BASED ON A TRANSFER LEARNING TECHNIQUE. THE 14 DIFFERENT YOGA POSTURES WERE COLLECTED FROM AN RGB CAMERA, AND EIGHT PARTICIPANTS WERE REQUIRED TO PERFORM EACH YOGA POSTURE 10 TIMES. DATA AUGMENTATION WAS APPLIED TO OVERSAMPLE AND PREVENT OVER-FITTING OF THE TRAINING DATASETS. SIX TRANSFER LEARNING MODELS (TL-VGG16-DA, TL-VGG19-DA, TL-MOBILENET-DA, TL-MOBILENETV2-DA, TL-INCEPTIONV3-DA, AND TL-DENSENET201-DA) WERE EXPLOITED FOR CLASSIFICATION TASKS TO SELECT THE OPTIMAL MODEL FOR THE YOGA COACHING SYSTEM, BASED ON EVALUATION METRICS. AS A RESULT, THE TL-MOBILENET-DA MODEL WAS SELECTED AS THE OPTIMAL MODEL, SHOWING AN OVERALL ACCURACY OF 98.43%, SENSITIVITY OF 98.30%, SPECIFICITY OF 99.88%, AND MATTHEWS CORRELATION COEFFICIENT OF 0.9831. THE STUDY PRESENTED A YOGA POSTURE COACHING SYSTEM THAT RECOGNIZED THE YOGA POSTURE MOVEMENT OF USERS, IN REAL TIME, ACCORDING TO THE SELECTED YOGA POSTURE GUIDANCE AND CAN COACH THEM TO AVOID INCORRECT POSTURES. 2022 6 903 27 EFFECTIVENESS OF A YOGA-BASED LIFESTYLE PROTOCOL (YLP) IN PREVENTING DIABETES IN A HIGH-RISK INDIAN COHORT: A MULTICENTER CLUSTER-RANDOMIZED CONTROLLED TRIAL (NMB-TRIAL). INTRODUCTION: THOUGH SEVERAL LINES OF EVIDENCE SUPPORT THE UTILITY OF YOGA-BASED INTERVENTIONS IN DIABETES PREVENTION, MOST OF THESE STUDIES HAVE BEEN LIMITED BY METHODOLOGICAL ISSUES, PRIMARILY SAMPLE SIZE INADEQUACY. HENCE, WE TESTED THE EFFECTIVENESS OF YOGA-BASED LIFESTYLE INTERVENTION AGAINST DIABETES RISK REDUCTION IN MULTICENTRE, LARGE COMMUNITY SETTINGS OF INDIA, THROUGH A SINGLE-BLIND CLUSTER-RANDOMIZED CONTROLLED TRIAL, NIYANTRITA MADHUMEHA BHARAT ABHIYAN (NMB). RESEARCH DESIGN AND METHODS: NMB-TRIAL IS A MULTICENTRE CLUSTER-RANDOMIZED TRIAL CONDUCTED IN 80 CLUSTERS [COMPOSED OF RURAL UNITS (VILLAGES) AND URBAN UNITS (CENSUS ENUMERATION BLOCKS)] RANDOMLY ASSIGNED IN A 1:1 RATIO TO INTERVENTION AND CONTROL GROUPS. PARTICIPANTS WERE INDIVIDUALS (AGE, 20-70 YEARS) WITH PREDIABETES (BLOOD HBA1C VALUES IN THE RANGE OF 5.7-6.4%) AND IDRS >/= 60. THE INTERVENTION INCLUDED THE PRACTICE OF YOGA-BASED LIFESTYLE MODIFICATION PROTOCOL (YLP) FOR 9 CONSECUTIVE DAYS, FOLLOWED BY DAILY HOME AND WEEKLY SUPERVISED PRACTICES FOR 3 MONTHS. THE CONTROL CLUSTER RECEIVED STANDARD OF CARE ADVICE FOR DIABETES PREVENTION. STATISTICAL ANALYSES WERE PERFORMED ON AN INTENTION-TO-TREAT BASIS, USING AVAILABLE AND IMPUTED DATASETS. THE PRIMARY OUTCOME WAS THE CONVERSION FROM PREDIABETES TO DIABETES AFTER THE YLP INTERVENTION OF 3 MONTHS (DIAGNOSED BASED UPON HBA1C CUTOFF >6.5%). SECONDARY OUTCOME INCLUDED REGRESSION TO NORMOGLYCEMIA WITH HBA1C <5.7%. RESULTS: A TOTAL OF 3380 (75.96%) PARTICIPANTS WERE FOLLOWED UP AT 3 MONTHS. AT 3 MONTHS POST-INTERVENTION, OVERALL, DIABETES DEVELOPED IN 726 (21.44%) PARTICIPANTS. YLP WAS FOUND TO BE SIGNIFICANTLY EFFECTIVE IN HALTING PROGRESSION TO DIABETES AS COMPARED TO STANDARD OF CARE; ADJUSTED RRR WAS 63.81(95% CI = 56.55-69.85). THE YLP ALSO ACCELERATED REGRESSION TO NORMOGLYCEMIA [ADJUSTED ODDS RATIO (ADJOR) = 1.20 (95% CI, 1.02-1.43)]. IMPORTANTLY, YOUNGER PARTICIPANTS (