Physical Factors Associated with Non-Specific Neck Pain: Correlations Among Pain, Disability, Posture, Endurance, and Compensatory Movement.

BACKGROUND This study was conducted to investigate physical risk factors in patients with non-specific neck pain. The correlations among pain intensity, pressure pain threshold, range of motion (ROM), and disability index were analyzed in 50 patients with non-specific neck pain at a hospital in Korea. MATERIAL AND METHODS We enrolled 50 patients diagnosed with non-specific neck pain by a doctor. All subjects were evaluated for pain intensity, pressure threshold, degree of disability, active range of motion (ROM) of the neck, upper cervical rotation ROM, muscular endurance of deep cervical flexor, compensatory movements for neck flexion, forward head posture, shoulder height difference, and rounded shoulder posture. The correlation between each variable was analyzed. RESULTS Pain intensity had a significant correlation between cervical rotation ROM, cervical flexion-rotation ROM, rounded shoulder posture, shoulder height difference, and forward head posture (P<.05). There was a significant correlation between the pressure pain threshold and the cervical extension ROM, cervical flexion-rotation ROM, and rounded shoulder height (P<.05). The disability index had a significant correlation between the cervical rotation ROM, cervical flexion-rotation ROM, rounded shoulder posture, and the compensatory movement of neck flexion (P<.05). CONCLUSIONS Physical risk factors for non-specific neck pain included cervical rotation ROM, upper cervical rotation ROM, rounded shoulder posture, shoulder height difference, and cervical flexion compensatory movements, which can affect pain intensity and pressure pain threshold.

Comparison of joint kinematics between upright front squat exercise and horizontal squat exercise performed on a short arm human centrifugation.

This study compared the joint kinematics between the front squat (FS) conducted in the upright (natural gravity) position and in the supine position on a short arm human centrifuge (SAHC). Male participants (N = 12) with no prior experience exercising on a centrifuge completed a FS in the upright position before (PRE) and after (POST) a FS exercise conducted on the SAHC while exposed to artificial gravity (AG). Participants completed, in randomized order, three sets of six repetitions with a load equal to body weight or 1.25 × body weight for upright squats, and 1 g and 1.25 g at the center of gravity (COG) for AG. During the terrestrial squats, the load was applied with a barbell. Knee (left/right) and hip (left/right) flexion angles were recorded with a set of inertial measurement units. AG decreased the maximum flexion angle (MAX) of knees and hips as well as the range of motion (ROM), both at 1 and 1.25 g. Minor adaptation was observed between the first and the last repetition performed in AG. AG affects the ability to FS in naïve participants by reducing MAX, MIN and ROM of the knees and hip.

Cardiac autonomic regulation following a 246-km mountain ultra-marathon: An observational study.

Physical exercise requires integrated autonomic and cardiovascular adjustments to maintain homeostasis. We aimed to observe acute posture-related changes in blood pressure, and apply a portable noninvasive monitor to measure the heart index for detecting arrhythmia among elite participants of a 246-km mountain ultra-marathon. Nine experienced ultra-marathoners (8 males and 1 female) participating in the Run Across Taiwan Ultra-marathon in 2018 were enrolled. The runners' Heart Spectrum Blood Pressure Monitor measurements were obtained in the standing and supine positions before and immediately after the race. Their high-sensitivity troponin T and N-terminal proB-type natriuretic peptide levels were analyzed 1 week before and immediately after the event. Heart rate was differed significantly in the immediate postrace assessment compared to the prerace assessment, in both the standing (P = .011; d = 1.19) and supine positions (P = .008; d = 1.35). Postural hypotension occurred in 4 (44.4%) individuals immediately postrace. In 3 out of 9 (33.3%) recruited finishers, the occurrence of premature ventricular complex signals in the standing position was detected; premature ventricular complex signal effect was observed in the supine position postrace in only 1 participant (11.1%). Premature ventricular complex signal was positively correlated with running speed (P = .037). Of the 6 individuals who completed the biochemical tests postrace, 2 (33.3%) had high-sensitivity troponin T and 6 (100%) had N-terminal proB-type natriuretic peptide values above the reference interval. A statistically significant increase was observed in both the high-sensitivity troponin T (P = .028; d = 1.97), and N-terminal proB-type natriuretic peptide (P = .028; d = 2.91) levels postrace compared to prerace. In conclusion, significant alterations in blood pressure and heart rate were observed in the standing position, and postexercise (postural) hypotension occurred among ultra-marathoners. The incidence of premature ventricular complexes was higher after the race than before.

IMU Data-Driven and PCA-Based Approach to Establish Quantifiable and Practically Applicable Measures for V2 Technique Elements in Cross-Country Skiing.

Quantifying movement coordination in cross-country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula-informed distinct elements of the V2 ski-skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set-down, leg push-off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element-specific principal component analyses (PCAs)-driven by the variance produced by the technique extremes-resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set-down, and leg push-off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski-skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists.

Incidence of medical adhesive-related skin injury: a reduction by changing posture.

OBJECTIVE: Medical adhesive-related skin injuries (MARSI), defined as skin damage associated with the use of medical adhesive products or devices, are a common and under-reported condition that compromises skin integrity. The prevention and management of MARSI that can occur around the needle insertion site of a chest wall implantable port in hospitalised patients with a tumour remain challenging issues. The aim of this study was to explore whether the incidence of MARSI could be reduced by changing the body position during dressing changes. METHOD: Participants were recruited between May 2019 and November 2020 in the oncology department of a tertiary hospital. Patients were randomly assigned to Group AB (supine followed by semi-recumbent position) and Group BA (semi-recumbent followed by supine position) with a standard intervening recovery interval of 21-28 days. Assessments for typical MARSI included itching, the combination of erythema and oedema, and blisters in the port area, and were graded according to the level of severity. RESULTS: The itch intensity was significantly lower in phase B (semi-recumbent) compared to phase A (supine) (2.35±1.985 versus 5.31±1.332, respectively; p<0.01). Similarly, the severity of erythema and oedema was less severe when comparing phase B to phase A: grade 0 (64.9% versus 10.5%, respectively); grade 1 (28.1% versus 19.3%, respectively); grade 2 (3.5% versus 7.0%, respectively); grade 3 (1.8% versus 45.6%, respectively); and grade 4 (1.8% versus 17.5%, respectively) (Z=5.703; p<0.01). Blisters were found far less frequently in phase B than phase A (1.8% versus 56.1%, respectively; p<0.01). CONCLUSION: The study provided statistically significant evidence that patients in a semi-recumbent position receiving dressing at a chest wall implantable port had fewer and less severe injection site MARSI than when in a supine position. DECLARATION OF INTEREST: The authors have no conflicts of interest to declare.

Spinal posture, mobility, and position sense in adolescents with chest wall deformities: a comparison of pectus excavatum, pectus carinatum and healthy peers.

PURPOSE: The study aimed to compare spinal posture, mobility, and position sense in adolescents with pectus excavatum (PE), pectus carinatum (PC), and healthy control (HC). METHODS: 22 with PE, 22 with PC, and 21 HC were included in the study. The spinal posture (thoracic kyphosis, lumbar lordosis, pelvic tilt, thoracic, lumbar, pelvic lateral tilt angles) and mobility (thoracic, lumbar, hip/sacral, and overall, in the sagittal and frontal plane) with the spinal mouse, and spinal position sense (repositing errors) with the inclinometer were assessed. RESULTS: The thoracic kyphosis angle of PE and PC was higher than in HC (p < 0.001; p = 0.001). Hip/sacral mobility in the sagittal plane was lower in the PE and PC than control, respectively (p < 0.001; p < 0.001). Overall sagittal spinal mobility (p:0.007) and hip/sacral mobility in the frontal plane (p:0.002) were lower in the PC than in HC. Overall frontal spinal mobility was lower in the PE and PC than in HC (p:0.002; p:0.014). The PE and PC repositing errors were higher (p < 0.001; p:0.014). CONCLUSION: The study found that adolescents with PE and PC had decreased spinal mobility, spinal alignment disorders, and a decline in spinal position sense. It is important not to overlook the spine during physical examinations of adolescents with chest wall deformities. In clinical practice, we suggest that adolescents with chest deformities should undergo a spine evaluation and be referred for physical therapy to manage spinal disorders.

Determinants of Perceived Comfort: Multi-Dimensional Thinking in Smart Bedding Design.

Sleep quality is an important issue of public concern. This study, combined with sensor application, aims to explore the determinants of perceived comfort when using smart bedding to provide empirical evidence for improving sleep quality. This study was conducted in a standard sleep laboratory in Quanzhou, China, from March to April of 2023. Perceived comfort was evaluated using the Subjective Lying Comfort Evaluation on a seven-point rating scale, and body pressure distribution was measured using a pressure sensor. Correlation analysis was employed to analyze the relationship between perceived comfort and body pressure, and multiple linear regression was used to identify the factors of perceived comfort. The results showed that body pressure was partially correlated with perceived comfort, and sleep posture significantly influenced perceived comfort. In addition, height, weight, and body mass index are common factors that influence comfort. The findings highlight the importance of optimizing the angular range of boards based on their comfort performance to adjust sleeping posture and equalize pressure distribution. Future research should consider aspects related to the special needs of different populations (such as height and weight), as well as whether users are elderly and whether they have particular diseases. The design optimization of the bed board division and mattress softness, based on traditional smart bedding, can improve comfort and its effectiveness in reducing health risks and enhancing health status.

Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor.

Recently, posture recognition technology has advanced rapidly. Herein, we present a novel posture angle calculation system utilizing a single inertial measurement unit and a spatial geometric equation to accurately identify the three-dimensional (3D) motion angles and postures of both the upper and lower limbs of the human body. This wearable system facilitates continuous monitoring of body movements without the spatial limitations or occlusion issues associated with camera-based methods. This posture-recognition system has many benefits. Providing precise posture change information helps users assess the accuracy of their movements, prevent sports injuries, and enhance sports performance. This system employs a single inertial sensor, coupled with a filtering mechanism, to calculate the sensor's trajectory and coordinates in 3D space. Subsequently, the spatial geometry equation devised herein accurately computed the joint angles for changing body postures. To validate its effectiveness, the joint angles estimated from the proposed system were compared with those from dual inertial sensors and image recognition technology. The joint angle discrepancies for this system were within 10° and 5° when compared with dual inertial sensors and image recognition technology, respectively. Such reliability and accuracy of the proposed angle estimation system make it a valuable reference for assessing joint angles.

Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach.

Grasp classification is pivotal for understanding human interactions with objects, with wide-ranging applications in robotics, prosthetics, and rehabilitation. This study introduces a novel methodology utilizing a multisensory data glove to capture intricate grasp dynamics, including finger posture bending angles and fingertip forces. Our dataset comprises data collected from 10 participants engaging in grasp trials with 24 objects using the YCB object set. We evaluate classification performance under three scenarios: utilizing grasp posture alone, utilizing grasp force alone, and combining both modalities. We propose Glove-Net, a hybrid CNN-BiLSTM architecture for classifying grasp patterns within our dataset, aiming to harness the unique advantages offered by both CNNs and BiLSTM networks. This model seamlessly integrates CNNs' spatial feature extraction capabilities with the temporal sequence learning strengths inherent in BiLSTM networks, effectively addressing the intricate dependencies present within our grasping data. Our study includes findings from an extensive ablation study aimed at optimizing model configurations and hyperparameters. We quantify and compare the classification accuracy across these scenarios: CNN achieved 88.09%, 69.38%, and 93.51% testing accuracies for posture-only, force-only, and combined data, respectively. LSTM exhibited accuracies of 86.02%, 70.52%, and 92.19% for the same scenarios. Notably, the hybrid CNN-BiLSTM proposed model demonstrated superior performance with accuracies of 90.83%, 73.12%, and 98.75% across the respective scenarios. Through rigorous numerical experimentation, our results underscore the significance of multimodal grasp classification and highlight the efficacy of the proposed hybrid Glove-Net architectures in leveraging multisensory data for precise grasp recognition. These insights advance understanding of human-machine interaction and hold promise for diverse real-world applications.

Learning Temporal-Spatial Contextual Adaptation for Three-Dimensional Human Pose Estimation.

Three-dimensional human pose estimation focuses on generating 3D pose sequences from 2D videos. It has enormous potential in the fields of human-robot interaction, remote sensing, virtual reality, and computer vision. Existing excellent methods primarily focus on exploring spatial or temporal encoding to achieve 3D pose inference. However, various architectures exploit the independent effects of spatial and temporal cues on 3D pose estimation, while neglecting the spatial-temporal synergistic influence. To address this issue, this paper proposes a novel 3D pose estimation method with a dual-adaptive spatial-temporal former (DASTFormer) and additional supervised training. The DASTFormer contains attention-adaptive (AtA) and pure-adaptive (PuA) modes, which will enhance pose inference from 2D to 3D by adaptively learning spatial-temporal effects, considering both their cooperative and independent influences. In addition, an additional supervised training with batch variance loss is proposed in this work. Different from common training strategy, a two-round parameter update is conducted on the same batch data. Not only can it better explore the potential relationship between spatial-temporal encoding and 3D poses, but it can also alleviate the batch size limitations imposed by graphics cards on transformer-based frameworks. Extensive experimental results show that the proposed method significantly outperforms most state-of-the-art approaches on Human3.6 and HumanEVA datasets.

Analysis of pig posture detection in group-housed pigs using deep learning-based mask scoring instance segmentation.

Pig posture is closely linked with livestock health and welfare. There has been significant interest among researchers in using deep learning techniques for pig posture detection. However, this task is challenging due to variations in image angles and times, as well as the presence of multiple pigs in a single image. In this study, we explore an object detection and segmentation algorithm based on instance segmentation scoring to detect different pig postures (sternal lying, lateral lying, walking, and sitting) and segment pig areas in group images, thereby enabling the identification of individual pig postures within a group. The algorithm combines a residual network with 50 layers and a feature pyramid network to extract feature maps from input images. These feature maps are then used to generate regions of interest (RoI) using a region candidate network. For each RoI, the algorithm performs regression to determine the location, classification, and segmentation of each pig posture. To address challenges such as missing targets and error detections among overlapping pigs in group housing, non-maximum suppression (NMS) is used with a threshold of 0.7. Through extensive hyperparameter analysis, a learning rate of 0.01, a batch size of 512, and 4 images per batch offer superior performance, with accuracy surpassing 96%. Similarly, the mean average precision (mAP) exceeds 83% for object detection and instance segmentation under these settings. Additionally, we compare the method with the faster R-CNN object detection model. Further, execution times on different processing units considering various hyperparameters and iterations have been analyzed.

Head poses and grimaces: Challenges for automated face identification algorithms?

In today's biometric and commercial settings, state-of-the-art image processing relies solely on artificial intelligence and machine learning which provides a high level of accuracy. However, these principles are deeply rooted in abstract, complex "black-box systems". When applied to forensic image identification, concerns about transparency and accountability emerge. This study explores the impact of two challenging factors in automated facial identification: facial expressions and head poses. The sample comprised 3D faces with nine prototype expressions, collected from 41 participants (13 males, 28 females) of European descent aged 19.96 to 50.89 years. Pre-processing involved converting 3D models to 2D color images (256 × 256 px). Probes included a set of 9 images per individual with head poses varying by 5° in both left-to-right (yaw) and up-and-down (pitch) directions for neutral expressions. A second set of 3,610 images per individual covered viewpoints in 5° increments from -45° to 45° for head movements and different facial expressions, forming the targets. Pair-wise comparisons using ArcFace, a state-of-the-art face identification algorithm yielded 54,615,690 dissimilarity scores. Results indicate that minor head deviations in probes have minimal impact. However, the performance diminished as targets deviated from the frontal position. Right-to-left movements were less influential than up and down, with downward pitch showing less impact than upward movements. The lowest accuracy was for upward pitch at 45°. Dissimilarity scores were consistently higher for males than for females across all studied factors. The performance particularly diverged in upward movements, starting at 15°. Among tested facial expressions, happiness and contempt performed best, while disgust exhibited the lowest AUC values.

Inter-observer reliability and anatomical landmarks for arm circumference to determine cuff size for blood pressure measurement.

Accurate arm circumference (AC) measurement is required for accurate blood pressure (BP) readings. Standards stipulate measuring arm circumference at the midpoint between the acromion process (AP) and the olecranon process. However, which part of the AP to use is not stipulated. Furthermore, BP is measured sitting but arm circumference is measured standing. We sought to understand how landmarking during AC measurement and body position affect cuff size selection. Two variations in measurement procedure were studied. First, AC was measured at the top of the acromion (TOA) and compared to the spine of the acromion (SOA). Second, standing versus seated measurements using each landmark were compared. AC was measured to the nearest 0.1 cm at the mid-point of the upper arm by two independent observers, blinded from each other's measurements. In 51 participants, the mean (±SD) mid-AC measurement using the anchoring landmarks TOA and SOA in the standing position were 32.4 cm (±6.18) and 32.1 cm (±6.07), respectively (mean difference of 0.3 cm). In the seated position, mean arm circumference was 32.2 (±6.10) using TOA and 31.1 (±6.03) using SOA (mean difference 1.1 cm). Kappa agreement for cuff selection in the standing position between TOA and SOA was 0.94 (p < 0.001). The landmark on the acromion process can change the cuff selection in a small percentage of cases. The overall impact of this landmark selection is small. However, standardizing landmark selection and body position for AC measurement could further reduce variability in cuff size selection during BP measurement and validation studies.

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types.

BACKGROUND: The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS. METHODS: A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels. RESULTS: A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen's kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types. CONCLUSIONS: The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods.

The impact of smartphone use duration and posture on the prevalence of hand pain among college students.

BACKGROUND: Excessive smartphone usage among students can lead to discomfort in their hands and fingers. This study investigates the impact of smartphone holding posture, duration of usage, and the prevalence of wrist and finger pain among university students. METHODS: This cross-sectional study involved 213 university students who were selected based on inclusion criteria. Data was collected through a demographic information questionnaire. Participants self-reported five different postures for holding and interacting with a smartphone. The prevalence, frequency, severity, and interference of wrist and finger discomfort were assessed using the Cornell Hand Discomfort Questionnaires (CHDQ). RESULTS: The study revealed that the average age of participants was 21.3 ± 2.2 years. On average, they had been using smartphones for 7.9 ± 3.1 years and spent an average of 4.9 ± 2.5 h daily holding them in their hands. In terms of discomfort, more than 25% of students reported pain in areas C (thumb finger), E (Palm Pollicis), and F (wrist) of the right hand, which was significantly related to the duration of holding the smartphone in that hand. Additionally, smartphone holding duration significantly affected areas D (palm) and F of the left hand, with over 11% of students experiencing discomfort. The most prevalent posture among students (41% of participants) involved holding the smartphone with the right hand only, with the thumb touching the screen. Notably, areas B (χ2 = 21.7), C (χ2 = 10.27), D (χ2 = 65.54), and E (χ2 = 59.49) of the right hand, as well as areas C (χ2 = 6.58) and E (χ2 = 44.28) of the left hand, exhibited significant associations with the postures of holding the smartphone. CONCLUSIONS: The duration of smartphone use and the postures in which it is held contribute to the prevalence of discomfort in the thumb area and related muscles among right-handed students.

Moving from stable standing to single-limb stance or an up-on-the-toes position: The importance of vision to dynamic balance control.

Understanding the contribution vision has to dynamic balance control may help in understanding where/why loss of balance occurs during everyday locomotion. The current study determined how body-centre-of-mass (BCoM) dynamics and postural stability when moving to and holding a single-limb-stance (SS) or an up-on-the-toes (UTT) position were affected by visual occlusion. From standing on a force platform, 18 adults (mean (SD) 26.7 (4.8) years; 1.73 (0.08) m; 84.0 (22.9) kg; 7 females) completed repeated trials (x3) with and without vision in which they moved to either a SS or an UTT position (order countered-balanced), and attempted to hold that position for 2 (SS) or 5 (UTT) seconds before returning to standing. UTT trials were also repeated at a fast speed, and SS trials were repeated using both the dominant and non-dominant limb. BCoM dynamics were assessed by analysing the displacement and peak velocity of the centre-of-pressure (CoP) when moving to and from the SS and UTT positions. Balance stability was the variability in the CoP displacement/velocity when holding these positions. Results indicate that under visual occlusion, the peak CoP velocity when moving to the SS or UTT position was reduced (ES, 0.67 and 0.68, respectively), suggesting greater caution. Both the variability in the CoP displacement/velocity when holding these positions and the peak CoP velocity when returning to flat-standing increased (SS: ES, 1.0 and 0.86, respectively; UTT: ES 1.26 and 0.66, respectively), suggesting, respectively, greater instability and poorer control. The poorer control in SS trials, occurred when returning to standing from the SS position held on the non-dominant limb, and correspondingly, the reduction in SS duration when vision was occluded was greater for the non-dominant limb trails (limb-vision interaction; p = 0.042). This suggests that movements initiated/controlled by the non-dominant limb are more reliant on visual feedback than those initiated/controlled by the dominant limb.

Therapeutic Interventions for Prevention of Musculoskeletal Pain Among Orthopedic Surgeons.

Orthopedic surgery is a physically demanding specialty. The factors contributing to musculoskeletal injury among surgeons often stem from positioning the patient, using non-ergonomic instruments, maintaining static postures, and performing repetitive movements. This article focuses on exercise techniques intended to combat the most common problematic static postures held during procedures. Each exercise explained in this article is organized into "preop," "intraop," and "postop" components. Preop includes strengthening movements, intraop provides postural recommendations, and postop focuses on mobilization and recovery. This article aims for efficient body conditioning, targeting the muscular posterior chain and supporting elements. [Orthopedics. 2024;47(4):e214-e216.].

Proposal and validation of a new approach in tele-rehabilitation with 3D human posture estimation: a randomized controlled trial in older individuals with sarcopenia.

OBJECTIVE: Through a randomized controlled trial on older adults with sarcopenia, this study compared the training effects of an AI-based remote training group using deep learning-based 3D human pose estimation technology with those of a face-to-face traditional training group and a general remote training group. METHODS: Seventy five older adults with sarcopenia aged 60-75 from community organizations in Changchun city were randomly divided into a face-to-face traditional training group (TRHG), a general remote training group (GTHG), and an AI-based remote training group (AITHG). All groups underwent a 3-month program consisting of 24-form Taichi exercises, with a frequency of 3 sessions per week and each session lasting 40 min. The participants underwent Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-meter walking pace, Timed Up and Go test (TUGT), and quality of life score (QoL) tests before the experiment, during the mid-term, and after the experiment. This study used SPSS26.0 software to perform one-way ANOVA and repeated measures ANOVA tests to compare the differences among the three groups. A significance level of p < 0.05 was defined as having significant difference, while p < 0.01 was defined as having a highly significant difference. RESULTS: (1) The comparison between the mid-term and pre-term indicators showed that TRHG experienced significant improvements in ASMI, 6-meter walking pace, and QoL (p < 0.01), and a significant improvement in TUGT timing test (p < 0.05); GTHG experienced extremely significant improvements in 6-meter walking pace and QoL (p < 0.01); AITHG experienced extremely significant improvements in ASMI, 6-meter walking pace, and QoL (p < 0.01), and a significant improvement in TUGT timing test (p < 0.05). (2) The comparison between the post-term and pre-term indicators showed that TRHG experienced extremely significant improvements in TUGT timing test (p < 0.01); GTHG experienced significant improvements in ASMI and TUGT timing test (p < 0.05); and AITHG experienced extremely significant improvements in TUGT timing test (p < 0.01). (3) During the mid-term, there was no significant difference among the groups in all tests (p > 0.05). The same was in post-term tests (p > 0.05). CONCLUSION: Compared to the pre-experiment, there was no significant difference at the post- experiment in the recovery effects on the muscle quality, physical activity ability, and life quality of patients with sarcopenia between the AI-based remote training group and the face-to-face traditional training group. 3D pose estimation is equally as effective as traditional rehabilitation methods in enhancing muscle quality, functionality and life quality in older adults with sarcopenia. TRIAL REGISTRATION: The trial was registered in ClinicalTrials.gov (NCT05767710).

A method for calculating vector forces at human-mattress interface during sleeping positions utilizing image registration.

The vector forces at the human-mattress interface are not only crucial for understanding the distribution of vertical and shear forces exerted on the human body during sleep but also serves as a significant input for biomechanical models of sleeping positions, whose accuracy determines the credibility of predicting musculoskeletal system loads. In this study, we introduce a novel method for calculating the interface vector forces. By recording indentations after supine and lateral positions using a vacuum mattress and 3D scanner, we utilize image registration techniques to align body pressure distribution with the mattress deformation scanning images, thereby calculating the vector force values for each unit area (36.25 mm × 36.25 mm). This method was validated through five participants attendance from two perspectives, revealing that (1) the mean summation of the vertical force components is 98.67% ± 7.21% body weight, exhibiting good consistency, and mean ratio of horizontal component force to body weight is 2.18% ± 1.77%. (2) the predicted muscle activity using the vector forces as input to the sleep position model aligns with the measured muscle activity (%MVC), with correlation coefficient over 0.7. The proposed method contributes to the vector force distribution understanding and the analysis of musculoskeletal loads during sleep, providing valuable insights for mattress design and evaluation.