We have constructed an insect-computer hybrid legged robot using a living beetle (Mecynorrhina torquata; Coleoptera). The protraction/retraction and levation/depression motions in both forelegs of the beetle were elicited by electrically stimulating eight corresponding leg muscles via eight pairs of implanted electrodes. To perform a defined walking gait (e.g., gallop), different muscles were individually stimulated in a predefined sequence using a microcontroller. Different walking gaits were performed by reordering the applied stimulation signals (i.e., applying different sequences). By varying the duration of the stimulation sequences, we successfully controlled the step frequency and hence the beetle's walking speed. To the best of our knowledge, this paper presents the first demonstration of living insect locomotion control with a user-adjustable walking gait, step length and walking speed.
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http://dx.doi.org/10.1098/rsif.2016.0060 | DOI Listing |
Age Ageing
January 2025
Aging Research Center, Department Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
Objective: We aimed to investigate the association of sociodemographic, clinical and functional characteristics with the volume of transitions and specific trajectories across living and care settings.
Methods: Using data from the Swedish National Study on Aging and Care in Kungsholmen study, we identified transitions across home (with or without social care), nursing homes, hospitals and postacute care facilities among 3021 adults aged 60+. Poisson and multistate models were used to investigate the association between sociodemographic, clinical and functional characteristics and both the overall volume and hazard ratios (HRs) of specific transitions.
J Biomech
January 2025
Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University Chengdu Sichuan Province China. Electronic address:
OpenCap, a smartphone-based markerless system, offers a cost-effective alternative to traditional marker-based systems for gait analysis. However, its kinematic measurement accuracy must be evaluated before widespread use in clinical practice. This study aimed to evaluate OpenCap for lower-limb joint angle measurements during walking in patients with knee osteoarthritis (OA) and to compare error metrics between patients and healthy controls.
View Article and Find Full Text PDFClin Biomech (Bristol)
January 2025
Department of Veterans Affairs, Center for Limb Loss and MoBility, Seattle, WA, USA; Department of Mechanical Engineering, University of Washington, Seattle, WA, USA. Electronic address:
Background: Ambulatory individuals with lower limb amputations often face challenges with body support, body propulsion, and balance control. Carrying an infant, toddler, backpack, or other load can exacerbate these challenges and highlights the importance of prescribing the most suitable prosthetic foot. The aim of this study was to examine the influence of five different prosthetic feet on walking performance during various load carriage conditions.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Wearable and Gait Assessment Research (WAGAR) Group, Prince of Wales Private Hospital, Randwick, NSW 2031, Australia.
Introduction: Gait analysis is a vital tool in the assessment of human movement and has been widely used in clinical settings to identify potential abnormalities in individuals. However, there is a lack of consensus on the normative values for gait metrics in large populations. The primary objective of this study is to establish a normative database of spatiotemporal gait metrics across various age groups, contributing to a broader understanding of human gait dynamics.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Centre for Automation and Robotics (CAR UPM-CSIC), Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI), Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain.
Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations.
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