In this work, we propose a new solution to 3D human pose estimation in videos. Instead of directly regressing the 3D joint locations, we draw inspiration from the human skeleton anatomy and decompose the task into bone direction prediction and bone length prediction, from which the 3D joint locations can be completely derived. Our motivation is the fact that the bone lengths of a human skeleton remain consistent across time. This promotes us to develop effective techniques to utilize global information across all the frames in a video for high-accuracy bone length prediction. Moreover, for the bone direction prediction network, we propose a fully-convolutional propagating architecture with long skip connections. Essentially, it predicts the directions of different bones hierarchically without using any time-consuming memory units (e.g. LSTM). A novel joint shift loss is further introduced to bridge the training of the bone length and bone direction prediction networks. Finally, we employ an implicit attention mechanism to feed the 2D keypoint visibility scores into the model as extra guidance, which significantly mitigates the depth ambiguity in many challenging poses. Our full model outperforms the previous best results on Human3.6M and MPI-INF-3DHP datasets, where comprehensive evaluation validates the effectiveness of our model.
Campbell Menzies, Richard Bowtell, Natalie Shur
et al.
ABSTRACT Sarcopenia describes the loss of muscle mass and function with age. The increase in prevalence of sarcopenia in women appears to coincide with the onset of menopause, which is characterized by large changes to the hormonal milieu such as decreased oestrogen and progesterone concentrations. Although the timing of menopause and sarcopenia may coincide, there is a lack of high‐quality evidence demonstrating a link between the two. This narrative review aims to assess evidence for the effects of menopause on muscle mass and muscle protein turnover. Longitudinal (n = 4/5) and cross‐sectional (n = 7/11) studies demonstrate a reduction in lean or muscle mass across the menopausal transition with −2.5% and −5.7% reductions in perimenopausal and postmenopausal women, respectively, compared to premenopausal women. Most of this evidence (n = 10/11) is taken through assessment of lean body mass via dual‐energy x‐ray absorptiometry (DXA), which may underestimate changes in muscle mass. Assessment on changes to muscle protein turnover is largely limited to short‐term measures of muscle protein synthesis (MPS), which may be elevated in older women versus younger women (n = 3/7) or age‐matched males (n = 4/5). MPS responses to anabolic stimuli, such as resistance exercise (n = 3/4) or protein ingestion (n = 3/6), may be blunted in older women. Evidence assessing muscle protein breakdown (MPB) is lacking; however, evidence from animal and cell models demonstrates the role of estradiol in suppressing MPB, which may contribute to an increase in MPB following menopause. Advancements in understanding the role of the menopausal transition in the regulation of muscle mass, and subsequent effectiveness of interventions such as exercise or exogenous hormone provision will enable healthy ageing and sarcopenia prevention in older women.
Diseases of the musculoskeletal system, Human anatomy
ABSTRACT Amyotrophic lateral sclerosis (ALS) is an incurable motor neuron disease characterized by progressive loss of motor neurons. Current clinically available drugs targeting neurons show minor survival extension and no motor improvement in ALS patients. This shifts the focus of ALS research toward non‐neuronal cells, particularly microglia, a critical driver of ALS pathogenesis. Highly druggable ion channels are key regulators of microglia function. Here, Hydrogen voltage gated channel 1 (HVCN1) was screened out as the most highly expressed ion channel in microglia, and was upregulated in microglia of SOD1G93A mice and patients. Deletion of HVCN1 in microglia increased motor neuron survival, rescued the innervated neuromuscular junctions in the muscle, reduced glial activation and decreased the level of both misfolded protein and myelin debris in the ALS mice. Importantly, these pathological improvements were translated into significant motor improvement and survival extension in the ALS mice, exhibiting better effects than the current clinical drugs. HVCN1 deletion enhanced microglia migration and their homeostatic state with elevated neurotrophic functions. Mechanistically, HVCN1 ablation promoted microglial migration via suppressing Akt signaling. Our results identify HVCN1 as a novel promising therapeutic target for ALS, opening a new avenue to further develop specific inhibitors for HVCN1 to alleviates ALS.
Abstract Nonspecific low back pain (NSLBP) is a predominant contributor to disability worldwide. Core stability exercises (CSEs) serve as a common intervention; however, their therapeutic effects may be augmented through the incorporation of adjunct modalities such as neuromuscular electrical stimulation. This randomized controlled trial aimed to evaluate whether the addition of Russian current (RC) stimulation to a CSE regimen enhances improvements in pain, functional capacity, muscle thickness, and spinal stability among patients diagnosed with NSLBP compared with a regimen comprising CSE alone. This single-blinded, randomized controlled trial was conducted at the outpatient clinic of Deraya University over a six-week intervention period. A total of fifty patients aged between 20 and 25 years with chronic NSLBP were recruited. The participants were randomly allocated into two groups via a closed-envelope. Group 1 (the study group, n = 25) received CSE and RC three times a week for six weeks, whereas Group 2 (the control group, n = 25) received CSE only. The primary outcomes assessed included pain intensity, measured via the visual analog scale (VAS), and functional disability, evaluated via the Oswestry Disability Index (ODI). The secondary outcomes included the muscle thicknesses of the transversus abdominis (TrA) and lumbar multifidus (LM), which were measured via ultrasonography, and lumbar spine stability, which was assessed via a Spinal Mouse device. The data were analyzed via mixed-model MANOVA for group and time effects, accompanied by post hoc Bonferroni correction. Between-group comparisons were performed via independent t tests, whereas within-group analyses were performed via paired t tests. There was a significant decrease in the VAS score and ODI and a significant increase in the stability score, TrA, and LM thickness, especially in group 1. The demographic characteristics of the groups were comparable at baseline (p > 0.05). Compared with Group 2 (CSE alone), Group 1 (CSE + RC) exhibited significantly greater improvements in the VAS score (MD: -1.04, 95% CI: -1.79–0.29, p = 0.008), ODI (MD: -9.76%, 95% CI: -12.13–7.39, p < 0.001), stability score (MD: 7.53%, 95% CI: 0.95–14.12, p = 0.02), and muscle thickness (e.g., right TrA: MD: 0.08 cm, 95% CI: 0.02–0.12, p = 0.004). There was a significant decrease in the VAS score and ODI and an increase in the stability score, TrA, and LM thickness, especially in group 1. Compared with core stability exercises alone, the Russian current accompanied by core stability exercises results in greater decreases in pain and disability and greater increases in functional ability, TrA and LM thickness, and lumbar spine stability. Trial registration: The study was registered on clinicaltrials.gov (NCT06495099) on 02/07/2024.
Adrian Arnaiz-Rodriguez, Nina Corvelo Benz, Suhas Thejaswi
et al.
Data-driven algorithmic matching systems promise to help human decision makers make better matching decisions in a wide variety of high-stakes application domains, such as healthcare and social service provision. However, existing systems are not designed to achieve human-AI complementarity: decisions made by a human using an algorithmic matching system are not necessarily better than those made by the human or by the algorithm alone. Our work aims to address this gap. To this end, we propose collaborative matching (comatch), a data-driven algorithmic matching system that takes a collaborative approach: rather than making all the matching decisions for a matching task like existing systems, it selects only the decisions that it is the most confident in, deferring the rest to the human decision maker. In the process, comatch optimizes how many decisions it makes and how many it defers to the human decision maker to provably maximize performance. We conduct a large-scale human subject study with $800$ participants to validate the proposed approach. The results demonstrate that the matching outcomes produced by comatch outperform those generated by either human participants or by algorithmic matching on their own. The data gathered in our human subject study and an implementation of our system are available as open source at https://github.com/Networks-Learning/human-AI-complementarity-matching.
Healthcare workers (HCWs) encounter challenges in hospitals, such as retrieving medical supplies quickly from crash carts, which could potentially result in medical errors and delays in patient care. Robotic crash carts (RCCs) have shown promise in assisting healthcare teams during medical tasks through guided object searches and task reminders. Limited exploration has been done to determine what communication modalities are most effective and least disruptive to patient care in real-world settings. To address this gap, we conducted a between-subjects experiment comparing the RCC's verbal and non-verbal communication of object search with a standard crash cart in resuscitation scenarios to understand the impact of robot communication on workload and attitudes toward using robots in the workplace. Our findings indicate that verbal communication significantly reduced mental demand and effort compared to visual cues and with a traditional crash cart. Although frustration levels were slightly higher during collaborations with the robot compared to a traditional cart, these research insights provide valuable implications for human-robot teamwork in high-stakes environments.
This late-breaking work presents a large-scale analysis of explainable AI (XAI) literature to evaluate claims of human explainability. We collaborated with a professional librarian to identify 18,254 papers containing keywords related to explainability and interpretability. Of these, we find that only 253 papers included terms suggesting human involvement in evaluating an XAI technique, and just 128 of those conducted some form of a human study. In other words, fewer than 1% of XAI papers (0.7%) provide empirical evidence of human explainability when compared to the broader body of XAI literature. Our findings underscore a critical gap between claims of human explainability and evidence-based validation, raising concerns about the rigor of XAI research. We call for increased emphasis on human evaluations in XAI studies and provide our literature search methodology to enable both reproducibility and further investigation into this widespread issue.
D. V. Dukov, A. N. Russkikh, A. D. Shabokha
et al.
The article presents an analysis of scientific literature devoted to the study of surgical anatomy of the ligamentous apparatus and metatarsal bones of the human foot. The literature covers the issues of macroanatomy and histology of the ligaments and metatarsal bones of the foot quite fully. At the same time, issues related to the same shape and size of bones, the relative position of ligaments, their histotopographic features are contradictory, which is associated with high variability, individual and age variability in combination with a number of social factors and features of the regions of residence. The work shows that at present, the data of domestic and foreign scientific literature on the anatomy and topography of the bones and ligaments of the metatarsal bones of the human foot are presented either by sectional studies or by the results of clinical observations using diagnostic equipment. The existing studies do not provide a comprehensive picture of the surgical anatomy of the ligamentous apparatus and metatarsal bones of the human foot. The article reflects the need for widespread use in fundamental anatomical studies of ligaments and metatarsal bones using the histotopographic method of research, quantitative and qualitative assessment of morphological parameters, which open up new possibilities for diagnosing pathological processes and developing new surgical techniques.
Our ability to build autonomous agents that leverage Generative AI continues to increase by the day. As builders and users of such agents it is unclear what parameters we need to align on before the agents start performing tasks on our behalf. To discover these parameters, we ran a qualitative empirical research study about designing agents that can negotiate during a fictional yet relatable task of selling a camera online. We found that for an agent to perform the task successfully, humans/users and agents need to align over 6 dimensions: 1) Knowledge Schema Alignment 2) Autonomy and Agency Alignment 3) Operational Alignment and Training 4) Reputational Heuristics Alignment 5) Ethics Alignment and 6) Human Engagement Alignment. These empirical findings expand previous work related to process and specification alignment and the need for values and safety in Human-AI interactions. Subsequently we discuss three design directions for designers who are imagining a world filled with Human-Agent collaborations.
As AI technology continues to advance, the importance of human-AI collaboration becomes increasingly evident, with numerous studies exploring its potential in various fields. One vital field is data science, including feature engineering (FE), where both human ingenuity and AI capabilities play pivotal roles. Despite the existence of AI-generated recommendations for FE, there remains a limited understanding of how to effectively integrate and utilize humans' and AI's knowledge. To address this gap, we design a readily-usable prototype, human\&AI-assisted FE in Jupyter notebooks. It harnesses the strengths of humans and AI to provide feature suggestions to users, seamlessly integrating these recommendations into practical workflows. Using the prototype as a research probe, we conducted an exploratory study to gain valuable insights into data science practitioners' perceptions, usage patterns, and their potential needs when presented with feature suggestions from both humans and AI. Through qualitative analysis, we discovered that the Creator of the feature (i.e., AI or human) significantly influences users' feature selection, and the semantic clarity of the suggested feature greatly impacts its adoption rate. Furthermore, our findings indicate that users perceive both differences and complementarity between features generated by humans and those generated by AI. Lastly, based on our study results, we derived a set of design recommendations for future human&AI FE design. Our findings show the collaborative potential between humans and AI in the field of FE.
Jindan Huang, Isaac Sheidlower, Reuben M. Aronson
et al.
Human-in-the-loop learning is gaining popularity, particularly in the field of robotics, because it leverages human knowledge about real-world tasks to facilitate agent learning. When people instruct robots, they naturally adapt their teaching behavior in response to changes in robot performance. While current research predominantly focuses on integrating human teaching dynamics from an algorithmic perspective, understanding these dynamics from a human-centered standpoint is an under-explored, yet fundamental problem. Addressing this issue will enhance both robot learning and user experience. Therefore, this paper explores one potential factor contributing to the dynamic nature of human teaching: robot errors. We conducted a user study to investigate how the presence and severity of robot errors affect three dimensions of human teaching dynamics: feedback granularity, feedback richness, and teaching time, in both forced-choice and open-ended teaching contexts. The results show that people tend to spend more time teaching robots with errors, provide more detailed feedback over specific segments of a robot's trajectory, and that robot error can influence a teacher's choice of feedback modality. Our findings offer valuable insights for designing effective interfaces for interactive learning and optimizing algorithms to better understand human intentions.
A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside human is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to human. In this work, we argue that a broader cause of suboptimal navigation performance near human is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
The rise of automation has provided an opportunity to achieve higher efficiency in manufacturing processes, yet it often compromises the flexibility required to promptly respond to evolving market needs and meet the demand for customization. Human-robot collaboration attempts to tackle these challenges by combining the strength and precision of machines with human ingenuity and perceptual understanding. In this paper, we conceptualize and propose an implementation framework for an autonomous, machine learning-based manipulator that incorporates human-in-the-loop principles and leverages Extended Reality (XR) to facilitate intuitive communication and programming between humans and robots. Furthermore, the conceptual framework foresees human involvement directly in the robot learning process, resulting in higher adaptability and task generalization. The paper highlights key technologies enabling the proposed framework, emphasizing the importance of developing the digital ecosystem as a whole. Additionally, we review the existent implementation approaches of XR in human-robot collaboration, showcasing diverse perspectives and methodologies. The challenges and future outlooks are discussed, delving into the major obstacles and potential research avenues of XR for more natural human-robot interaction and integration in the industrial landscape.
Humanity-centered design is a concept of emerging interest in HCI, one motivated by the limitations of human-centered design. As discussed to date, humanity-centered design is compatible with but goes beyond human-centered design in that it considers entire ecosystems and populations over the long term and centers participatory design. Though the intentions of humanity-centered design are laudable, current articulations of humanity-centered design are incoherent in a number of ways, leading to questions of how exactly it can or should be implemented. In this article, I delineate four ways in which humanity-centered design is incoherent, which can be boiled down to a tendency toward hubris, and propose a more fruitful way forward, a humble approach to humanity-centered design. Rather than a contradiction in terms, "humility" here refers to an organic, piecemeal, patterns-based approach to design that will be good for our being on this earth.
Abstract Background Hand grip strength (HGS) is a powerful indicator of sarcopenia and other adverse health outcomes. Normative values for HGS for general Chinese people with a broad age spectrum are lacking. This study aims to establish normative values of HGS and explore the correlations between HGS and body composition among unselected people aged 8–80 in China. Methods From 2012 to 2017, 39 655 participants aged 8–80 years in the China National Health Survey were included. Absolute HGS was measured using a Jamar dynamometer. The relative HGS was normalized by body mass index. Body composition indexes included body mass index, body fat percentage, muscle mass, fat mass index (FMI) and muscle mass index (MMI). Sex‐specific smoothed centile tables for the P1, P5, P25, P50, P75, P95 and P99 centiles of HGS and body composition were generated using lambda‐mu‐sigma method. The correlations between muscle strength and body composition were estimated by partial Spearman correlation analysis. Results The median values (25th and 75th percentile) of HGS in boys and girls (8–19 years old) were 22 (14, 34) kg and 18 (12, 22) kg, respectively; in men and women aged 20–80 were 39 (33, 44) kg and 24 (20, 27) kg, respectively. Values of upper and lower HGS across ages had three periods: an increase to a peak in the 20 s in men (with the 5th and 95th values of 30 and 55 kg, respectively) and 30 s in women (with the 5th and 95th values of 18 and 34 kg, respectively), preservation through midlife (20s–40 s), and then a decline after their 50 s. The lowest HGS values in both sexes were in the 70‐ to 80‐year‐old group, with the 5th and 95th percentile values of 16 and 40 kg in men, and 10 and 25 kg in women. There were substantial sex differences in body composition in the life course (all P values <0.001). In ageing, the decrease of muscle strength was faster than that of muscle mass in both sexes. The correlations between muscle mass and HGS were most robust than other correlations, especially in women (0.68 vs. 0.50), children and adolescents. Conclusions Our study established the age‐ and sex‐specific percentile reference values for hand grip strength in an unselected Chinese population across a broad age‐spectrum. The rich data can facilitate the practical appraisal of muscle strength and promote early prediction of sarcopenia and other impairments associated with neuromuscular disorders.
Diseases of the musculoskeletal system, Human anatomy
Hojjat Radinmehr, Nahid Radnia, Azade Tabatabaei
et al.
Symptoms of overactive bladder syndrome (OAB), including urinary incontinence, affect a person's quality of life and cause many personal, social and economic problems. Patients were randomly divided into three groups and received transcutaneous tibial nerve stimulation (cTTNS) with fixed parameters or with variable parameters (vTTNS) and Solifenacin drug. The main outcomes including quality of life questionnaire and OAB score and other secondary outcomes were evaluated before and after treatment for 6 weeks. ANOVA test did not show any significant difference between the three groups in quality of life score (p=0.672), OAB symptom score (p=0.159) and incontinence severity (p=0.422). The t-test demonstrated that the post treatment average quality of life score, OAB score, and incontinence severity were significantly different when compared with before treatment in all three groups (p < 0.05). All three methods were effective in treating symptoms of OAB. However, based on the clinical symptoms, cTTNS is recommended as a preferred and acceptable and safe strategy for the treatment of OAB in women over 50 years old.