TransKinect: a computer vision and machine learning clinical decision support system for automatic independent wheelchair transfer technique assessment.

BACKGROUND: Physical and occupational therapists provide routine care for manual wheelchair users and are responsible for training and assessing the quality of transfers. These transfers can produce large loads on the upper extremity joints if improper sitting-pivot-technique is used. Methods to assess quality of transfers include the Transfer Assessment Instrument, a clinically validated tool derived from quantitative biomechanical features; however, adoption of this tool is low due to the complex usage requirements and speed of typical transfers. OBJECTIVE: The objective of this study is to develop and validate a computer vison and machine learning solution to better implement the Transfer Assessment Instrument in clinical settings. METHODS: The prototype system, TransKinect, consists of an infrared depth sensor and a custom software application; usability testing was carried out with fifteen therapists who performed two transfer assessments with the TransKinect. Proficiency in using features, usability, acceptability and satisfaction were analysed with validated surveys and themes were extracted from the qualitative feedback. RESULTS: The therapists were able to successfully complete the transfer quality assessments with 86.7 ± 5.4% proficiency. Total scores for System Usability Scale (77.6 ± 14.7%) and Questionnaire for User Interface Satisfaction (83.5 ± 8.7%) indicated that the system was usable and satisfactory. Qualitative feedback indicated that TransKinect was user-friendly, easy to learn, and had high potential. DISCUSSION: The results support TransKinect as a potential clinical decision support system for therapists for the comprehensive assessment of independent transfer technique. Future research is needed to investigate the utility and acceptance of TransKinect in real clinical environments. Implications for RehabilitationMachine learning and computer vision can be used to analyze transfer techniqueTransKinect is a usable and user-friendly means for therapists to automate analysisSummary reports and videos of transfers show high potential for clinical useAdoption of TransKinect can increase quality of care for manual wheelchair users.

The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media.

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin-destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales-10-100 m and 100-3000 m-to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.

MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture.

This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using 'filtered' estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model's efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency.

Kinematic analysis of an unrestrained passenger in an autonomous vehicle during emergency braking.

Analyzing human body movement is a critical aspect of biomechanical studies in road safety. While most studies have traditionally focused on assessing the head-neck system due to the restraint provided by seat belts, it is essential to examine the entire pelvis-thorax-head kinematic chain when these body regions are free to move. The absence of restraint systems is prevalent in public transport and is also being considered for future integration into autonomous vehicles operating at low speeds. This article presents an experimental study examining the movement of the pelvis, thorax and head of 18 passengers seated without seat belts during emergency braking in an autonomous bus. The movement was recorded using a video analysis system capturing 100 frames per second. Reflective markers were placed on the knee, pelvis, lumbar region, thorax, neck and head, enabling precise measurement of the movement of each body segment and the joints of the lumbar and cervical spine. Various kinematic variables, including angles, displacements, angular velocities and accelerations, were measured. The results delineate distinct phases of body movement during braking and elucidate the coordination and sequentiality of pelvis, thorax and head rotation. Additionally, the study reveals correlations between pelvic rotation, lumbar flexion, and vertical trunk movement, shedding light on their potential impact on neck compression. Notably, it is observed that the elevation of the C7 vertebra is more closely linked to pelvic tilt than lumbar flexion. Furthermore, the study identifies that the maximum angular acceleration of the head and the maximum tangential force occur during the trunk's rebound against the seatback once the vehicle comes to a complete stop. However, these forces are found to be insufficient to cause neck injury. While this study serves as a preliminary investigation, its findings underscore the need to incorporate complete trunk kinematics, particularly of the pelvis, into braking and impact studies, rather than solely focusing on the head-neck system, as is common in most research endeavors.

TAT-HUM: Trajectory analysis toolkit for human movements in Python.

Human movement trajectories can reveal useful insights regarding the underlying mechanisms of human behaviors. Extracting information from movement trajectories, however, can be challenging because of their complex and dynamic nature. The current paper presents a Python toolkit developed to help users analyze and extract meaningful information from the trajectories of discrete rapid aiming movements executed by humans. This toolkit uses various open-source Python libraries, such as NumPy and SciPy, and offers a collection of common functionalities to analyze movement trajectory data. To ensure flexibility and ease of use, the toolkit offers two approaches: an automated approach that processes raw data and generates relevant measures automatically, and a manual approach that allows users to selectively use different functions based on their specific needs. A behavioral experiment based on the spatial cueing paradigm was conducted to illustrate how one can use this toolkit in practice. Readers are encouraged to access the publicly available data and relevant analysis scripts as an opportunity to learn about kinematic analysis for human movements.

Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting.

The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human-machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs.

Dança do Ventre: imagens contemporâneas / Danza del vientre: imágenes contemporáneas / Belly dance: contemporary images

A história da dança do ventre costuma ser atribuída às práticas ritualísticas da Antiguidade no Oriente Médio. Essa narrativa dificilmente sustenta-se como fato histórico. Entretanto, algumas imagens desempenham papel importante ao gerar sentidos sobre essa dança enquanto manifestação expressiva na atualidade. O objetivo do artigo é investigar os principais fatores relacionados ao movimentar-se presentes na dança do ventre, levando em conta seus aspectos simbólicos e expressivos, de forma a identificar os sentidos desta dança para bailarinas e bailarinos brasileiros. O estudo segue uma orientação fenomenológica que busca descrever as imagens sobre a dança do ventre reveladas em pesquisas de campo. Narrativas associadas ao feminino são recorrentes e sugerem uma elaboração potente, que dialoga com a ancestralidade e se atualiza de forma contemporânea. A dança ganha um sentido existencial e único para quem dança, mas revela um potencial expressivo que é universal.

The history of belly dancing is often attributed to ancient ritualistic practices in the Middle East. This narrative hardly holds up as a historical fact. However, some images are essential in generating meanings about this dance as an expressive manifestation today. The article's objective is to investigate the main factors related to movement present in belly dancing, taking into account its symbolic and expressive aspects, to identify the meanings of this dance for Brazilian dancers. The study follows a phenomenological orientation that seeks to describe the images of belly dancing revealed in field research. Narratives associated with the feminine are recurrent and suggest a powerful elaboration, which dialogues with ancestry and is updated in a contemporary way. The dance takes on an existential and unique meaning for those who dance but reveals a universal expressive potential.

La historia de la danza del vientre a menudo se atribuye a antiguas prácticas rituales femeninas en las sociedades del Medio Oriente. Históricamente, estas narraciones no se sostienen. Aun así, estas imágenes juegan un papel importante en la afirmación de un arquetipo femenino, generando significados sobre esta danza y su importancia como manifestación expresiva en la actualidad. Este estudio fenomenológico busca describir y profundizar en estas y otras imágenes reveladas en testimonios e investigaciones de campo. A partir de esto, buscamos comprender cómo la práctica de la danza del vientre transforma percepciones y sentidos sobre el cuerpo y el mundo de quien baila. Lo femenino de la danza parece fortalecerse colectivamente y sugiere una intención de cambio constante, a partir de experiencias corporales, en contacto con el mundo, desde un lenguaje propio: una práctica de negociación y elaboración de un femenino mucho más contemporáneo que el mito supone.

Running Economy in the Vertical Kilometer.

New and promising variables are being developed to analyze performance and fatigue in trail running, such as mechanical power, metabolic power, metabolic cost of transport and mechanical efficiency. The aim of this study was to analyze the behavior of these variables during a real vertical kilometer field test. Fifteen trained trail runners, eleven men (from 22 to 38 years old) and four women (from 19 to 35 years old) performed a vertical kilometer with a length of 4.64 km and 835 m positive slope. During the entire race, the runners were equipped with portable gas analyzers (Cosmed K5) to assess their cardiorespiratory and metabolic responses breath by breath. Significant differences were found between top-level runners versus low-level runners in the mean values of the variables of mechanical power, metabolic power and velocity. A repeated-measures ANOVA showed significant differences between the sections, the incline and the interactions between all the analyzed variables, in addition to differences depending on the level of the runner. The variable of mechanical power can be statistically significantly predicted from metabolic power and vertical net metabolic COT. An algebraic expression was obtained to calculate the value of metabolic power. Integrating the variables of mechanical power, vertical velocity and metabolic power into phone apps and smartwatches is a new opportunity to improve performance monitoring in trail running.

Human Movement Recognition Based on 3D Point Cloud Spatiotemporal Information from Millimeter-Wave Radar.

Human movement recognition is the use of perceptual technology to collect some of the limb or body movements presented. This practice involves the use of wireless signals, processing, and classification to identify some of the regular movements of the human body. It has a wide range of application prospects, including in intelligent pensions, remote health monitoring, and child supervision. Among the traditional human movement recognition methods, the widely used ones are video image-based recognition technology and Wi-Fi-based recognition technology. However, in some dim and imperfect weather environments, it is not easy to maintain a high performance and recognition rate for human movement recognition using video images. There is the problem of a low recognition degree for Wi-Fi recognition of human movement in the case of a complex environment. Most of the previous research on human movement recognition is based on LiDAR perception technology. LiDAR scanning using a three-dimensional static point cloud can only present the point cloud characteristics of static objects; it struggles to reflect all the characteristics of moving objects. In addition, due to its consideration of privacy and security issues, the dynamic millimeter-wave radar point cloud used in the previous study on the existing problems of human body movement recognition performance is better, with the recognition of human movement characteristics in non-line-of-sight situations as well as better protection of people's privacy. In this paper, we propose a human motion feature recognition system (PNHM) based on spatiotemporal information of the 3D point cloud of millimeter-wave radar, design a neural network based on the network PointNet++ in order to effectively recognize human motion features, and study four human motions based on the threshold method. The data set of the four movements of the human body at two angles in two experimental environments was constructed. This paper compares four standard mainstream 3D point cloud human action recognition models for the system. The experimental results show that the recognition accuracy of the human body's when walking upright can reach 94%, the recognition accuracy when moving from squatting to standing can reach 84%, that when moving from standing to sitting can reach 87%, and the recognition accuracy of falling can reach 93%.

Differences and relationship in functional movement screen (FMS™) scores and physical fitness in males and female semi-professional soccer players.

Background: Soccer is the world's most popular sport for both men and women. Tests of athletic and functional performance are commonly used to assess physical ability and set performance goals. The Functional Movement Screen (FMS™) is a widely used seven-test battery developed by practitioners to provide interpretable measure of movement quality. The main objective of the present study was twofold, to analyze the relationship between FMS™ results from male and female soccer players and to compare their physical performance in different tests. Methods: A total of twenty-eight semi-professional soccer players: fourteen male (age: 21.29 ± 1.64 years; weight: 70.66 ± 5.29 kg; height: 171.86 ± 5.35 cm; BMI: 20.90 ± 2.22 kg/m2) and fourteen females (age: 20.64 ± 1.98 years; weight: 63.44 ± 5.83 kg; height: 166.21 ± 12.18 cm; BMI: 23.02 ± 2.50 kg/m2) were recruited for this study. A paired sample t-test was used for determining differences as a repeated measures analysis. All the participants conducted the following tests: The Functional Movement Test (FMS™), 10-m linear sprint, 5-0-5 COD Test and Yo-Yo Intermittent Recovery Test-Level 1 (YYIRT Level 1). Results: A t-test with data from 505 COD (change of direction) test showed significant differences between groups, p = 0.001, d = 1.11, revealing faster times in male soccer players (2.50 ± 0.19) in respect with female soccer players (2.70 ± 0.17). Crucially, a t-test with data from FMS did not reveal significant differences between groups. Multiple regression for V02max revealed significant effects (r = 0.55, r2 = 0.30, adjusted r2 = 0.24, F = 5.21, p = 0.04 and standard error = 2.20). On the other hand, multiple regression for 10-m sprint showed significant effects (r = 0.58, r2 = 0.33, adjusted r2 = 0.28, F = 5.98, p = 0.03). The impact of these factors on the correlation between FMS™ scores and physical performance measures can vary among individuals. Discussion/Conclusion: This study demonstrates the necessity of utilizing and applying multiple field-based tests to evaluate the movement and capabilities of physical performance in sports. Crucially, consider individual variations and factors such as training background, fitness level, and sport-specific demands when interpreting the relationship between the FMS™ and physical performance in both sexes.

Perceptions of learning and teaching human movement in physiotherapy: A systematic review and metasynthesis of qualitative studies.

PURPOSE: Human movement is essential for health and well-being. Understanding human movement is pivotal in physiotherapy, but also an important element of physiotherapy education. This review identified, critically appraised, and synthesized the available evidence on learning and teaching human movement in physiotherapy as perceived by students, therapists, and instructors. METHODS: The databases MEDLINE, CINAHL, ERIC, PsycINFO, MEDIC and FINNA, were searched. The search was conducted in March/April 2020 and updated in March 2022. The systematic review followed the JBI methodology for systematic reviews of qualitative evidence and was conducted in accordance with an a priori protocol. RESULTS: The overall quality of the 17 included studies was scored low on ConQual but dependability and credibility were rated as moderate. Four synthesized findings aggregated from 17 categories and 147 findings described the perceived significance of 1) being present in movement, 2) movement quality, 3) movement transfer, and 4) contextual factors for the learning or teaching of human movement in physiotherapy. CONCLUSION: The synthesized findings indicate that the perceived significance of contextual factors, movement quality and transfer, and being present in movement should be considered in all learning and teaching of movement in physiotherapy. However, the evidence of the review findings was evaluated as low-level, which should be considered when applying these results to physiotherapy education or practice.

Perspectives on human movement considerations in indoor airflow assessment: a comprehensive data-driven systematic review.

Understanding particle dispersion characteristics in indoor environments is crucial for revising infection prevention guidelines through optimized engineering control. The secondary wake flow induced by human movements can disrupt the local airflow field, which enhances particle dispersion within indoor spaces. Over the years, researchers have explored the impact of human movement on indoor air quality (IAQ) and identified noteworthy findings. However, there is a lack of a comprehensive review that systematically synthesizes and summarizes the research in this field. This paper aims to fill that gap by providing an overview of the topic and shedding light on emerging areas. Through a systematic review of relevant articles from the Web of Science database, the study findings reveal an emerging trend and current research gaps on the topic titled Impact of Human Movement in Indoor Airflow (HMIA). As an overview, this paper explores the effect of human movement on human microenvironments and particle resuspension in indoor environments. It delves into the currently available methods for assessing the HMIA and proposes the integration of IoT sensors for potential indoor airflow monitoring. The present study also emphasizes incorporating human movement into ventilation studies to achieve more realistic predictions and yield more practical measures. This review advances knowledge and holds significant implications for scientific and public communities. It identifies future research directions and facilitates the development of effective ventilation strategies to enhance indoor environments and safeguard public health.

Dynamical Analyses Show That Professional Archers Exhibit Tighter, Finer and More Fluid Dynamical Control Than Neophytes.

Quantifying the dynamical features of discrete tasks is essential to understanding athletic performance for many sports that are not repetitive or cyclical. We compared three dynamical features of the (i) bow hand, (ii) drawing hand, and (iii) center of mass during a single bow-draw movement between professional and neophyte archers: dispersion (convex hull volume of their phase portraits), persistence (tendency to continue a trend as per Hurst exponents), and regularity (sample entropy). Although differences in the two groups are expected due to their differences in skill, our results demonstrate we can quantify these differences. The center of mass of professional athletes exhibits tighter movements compared to neophyte archers (6.3 < 11.2 convex hull volume), which are nevertheless less persistent (0.82 < 0.86 Hurst exponent) and less regular (0.035 > 0.025 sample entropy). In particular, the movements of the bow hand and center of mass differed more between groups in Hurst exponent analysis, and the drawing hand and center of mass were more different in sample entropy analysis. This suggests tighter neuromuscular control over the more fluid dynamics of the movement that exhibits more active corrections that are more individualized. Our work, therefore, provides proof of principle of how well-established dynamical analysis techniques can be used to quantify the nature and features of neuromuscular expertise for discrete movements in elite athletes.

Moving through silence in dance: A neural perspective.

The word "silence" typically refers to the auditory modality, signifying an absence of sound or noise, being quiet. One may then ask: could we attribute the notion of silence to the domain of dance, e.g., when a movement is absent and the dancer stops moving? Is it at all useful to think in terms of silence when referring to dance? In this chapter, my exploration of these questions is based on recent studies in brain research, which demonstrate the remarkable facility of specific regions in the human brain to perceive visually referred biological and, in particular, human motion, leading to prediction of future movements of the human body. I will argue that merely ceasing motion is an insufficient condition for creating a perception of silence in the mind of a spectator of dance. Rather, the experience of silence in dance is a special situation where the static position of the dancer does not imply motion, and is unlikely to evoke interpretation of the intentions or the emotional expression of the dancer. For this to happen, the position of the dancer, while being still, should be held effortlessly, aimlessly, and with a minimal expression of emotion and intention. Furthermore, I suggest that dynamics, repetitive movement (such as that of Sufi whirling dervishes), can also be perceived as silence in dance because of the high level of predictability and evenness of the movement. These moments of silence in dance, which are so rare in our daily lives, invite us to experience the human body from a new, "out of the box" perspective that is the essence of all the arts.

Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review.

Integrated Ultra-wideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensor systems have been gaining popularity for pedestrian tracking and indoor localization applications, mainly due to their complementary error characteristics that can be exploited to achieve higher accuracies via a data fusion approach. These integrated sensor systems have the potential for improving the ambulatory 3D analysis of human movement (estimating 3D kinematics of body segments and joints) over systems using only on-body MIMUs. For this, high accuracy is required in the estimation of the relative positions of all on-body integrated UWB/MIMU sensor modules. So far, these integrated UWB/MIMU sensors have not been reported to have been applied for full-body ambulatory 3D analysis of human movement. Also, no review articles have been found that have analyzed and summarized the methods integrating UWB and MIMU sensors for on-body applications. Therefore, a comprehensive analysis of this technology is essential to identify its potential for application in 3D analysis of human movement. This article thus aims to provide such a comprehensive analysis through a structured technical review of the methods integrating UWB and MIMU sensors for accurate position estimation in the context of the application for 3D analysis of human movement. The methods used for integration are all summarized along with the accuracies that are reported in the reviewed articles. In addition, the gaps that are required to be addressed for making this system applicable for the 3D analysis of human movement are discussed.

Impacts on Human Movement in Australian Cities Related to the COVID-19 Pandemic.

No studies have yet examined high-resolution shifts in the spatial patterns of human movement in Australia throughout 2020 and 2021, a period coincident with the repeated enactment and removal of varied governmental restrictions aimed at reducing community transmission of SARS-CoV-2. We compared overlapping timeseries of COVID-19 pandemic-related restrictions, epidemiological data on cases and vaccination rates, and high-resolution human movement data to characterize population-level responses to the pandemic in Australian cities. We found that restrictions on human movement and/or mandatory business closures reduced the average population-level weekly movement volumes in cities, as measured by aggregated travel time, by almost half. Of the movements that continued to occur, long movements reduced more dramatically than short movements, likely indicating that people stayed closer to home. We also found that the repeated lockdowns did not reduce their impact on human movement, but the effect of the restrictions on human movement waned as the duration of restrictions increased. Lastly, we found that after restrictions ceased, the subsequent surge in SARS-CoV-2 transmission coincided with a substantial, non-mandated drop in human movement volume. These findings have implications for public health policy makers when faced with anticipating responses to restrictions during future emergency situations.

Aerosol exchange between pressure-equilibrium rooms induced by door motion and human movement.

It is now widely recognised that aerosol transport is major vector for transmission of diseases such as COVID-19, and quantification of aerosol transport in the built environment is critical to risk analysis and management. Understanding the effects of door motion and human movement on the dispersion of virus-laden aerosols under pressure-equilibrium conditions is of great significance to the evaluation of infection risks and development of mitigation strategies. This study uses novel numerical simulation techniques to quantify the impact of these motions upon aerosol transport and provides valuable insights into the wake dynamics of swinging doors and human movement. The results show that the wake flow of an opening swinging door delays aerosol escape, while that of a person walking out entrains aerosol out of the room. Aerosol escape caused by door motion mainly happens during the closing sequence which pushes the aerosols out. Parametric studies show that while an increased door swinging speed or human movement speed can enhance air exchange across the doorway, the cumulative aerosol exchange across the doorway is not clearly affected by the speeds.

Human locomotion over obstacles reveals real-time prediction of energy expenditure for optimized decision-making.

Despite decades of evidence revealing a multitude of ways in which animals are adapted to minimize the energy cost of locomotion, little is known about how energy expenditure shapes adaptive gait over complex terrain. Here, we show that the principle of energy optimality in human locomotion can be generalized to complex task-level locomotor behaviours requiring advance decision-making and anticipatory control. Participants completed a forced-choice locomotor task requiring them to choose between discrete multi-step obstacle negotiation strategies to cross a 'hole' in the ground. By modelling and analysing mechanical energy cost of transport for preferred and non-preferred manoeuvres over a wide range of obstacle dimensions, we showed that strategy selection was predicted by relative energy cost integrated across the complete multi-step task. Vision-based remote sensing was sufficient to select the strategy associated with the lowest prospective energy cost in advance of obstacle encounter, demonstrating the capacity for energetic optimization of locomotor behaviour in the absence of online proprioceptive or chemosensory feedback mechanisms. We highlight the integrative hierarchic optimizations that are required to facilitate energetically efficient locomotion over complex terrain and propose a new behavioural level linking mechanics, remote sensing and cognition that can be leveraged to explore locomotor control and decision-making.