Bio-Inspired, Biomedical, Surgical, Social and AI-Integrated Bio-Mechanical Robotics

Topic Information

Dear Colleagues,

We are thrilled to announce the Topic Issue "Bio-Inspired, Biomedical, Surgical, Social and AI-Integrated Bio-Mechanical Robotics", co-organized with the 17th International Conference on Social Robotics + BioMed (ICSR + BioMed). This initiative converges biological inspiration, clinical innovation, AI, and robotics to address the fragmented nature of advancements in these fields, such as decoding the complex interplay of plant-emitted volatile compounds (VOCs) and their effects on human health. Just as forests harbor unseen synergies between ecology and wellness, bio-mechanical robotics holds untapped potential at the intersection of bioinspired design (e.g., skeletal mimicry), AI-driven diagnostics, surgical precision, and social robotics for aging populations.

While AI and bioinspired technologies have shown promise in isolation—such as AI-enhancing medical imaging or soft robots mimicking tissue elasticity—their integration remains underexplored, akin to understanding only part of a forest’s ecosystem. This Topic seeks to bridge these domains, focusing on how cross-disciplinary collaboration can unlock the "real potential" of bio-related robots: systems that adapt to biological dynamics, enhance human–robot interaction in healthcare, and address societal challenges like aging populations or disability support.

In an era of global healthcare challenges, this initiative aims to position bio-mechanical robotics as a holistic solution, such as forest bathing evolved from tradition to science. We envision contributions that inspire new paradigms, such as "robo-therapies" prescribed for rehabilitation or AI-bioinspired systems that mimic biological healing processes. By uniting diverse perspectives, we seek to create technologies that are not only innovative but also human-centric—adaptive, ethical, and accessible to all.

Join us in shaping this frontier. Submissions are welcome from analytical methods, biomolecular interactions, or AI-bioinspired designs, with a focus on solutions that resonate across labs, clinics, and communities. Together, we will decode the language of bio-mechanical robotics to nurture a future where technology and humanity thrive in harmony.

Prof. Dr. Yanen Wang
Prof. Dr. Chenguang Yang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biomimetics biomimetics	3.9	4.2	2016	17 Days	CHF 2200	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Gels gels	5.3	7.6	2015	13.5 Days	CHF 2100	Submit
Robotics robotics	3.3	7.7	2012	23.7 Days	CHF 1800	Submit
Technologies technologies	3.6	8.5	2013	19.1 Days	CHF 1800	Submit

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Published Papers (5 papers)

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All Biomimetics Electronics Gels Robotics Technologies

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31 pages, 24044 KB  

Open AccessSystematic Review

A Systematic Literature Review on Intelligent Soft Hand Exoskeleton Robots: Artificial Intelligence-Enabled Personalisation, Adaptation, and Design Considerations

by Seena Joseph, Wai Keung Fung, Tony Punnoose Valayil, Rajan Prasad and Tim Bashford

Robotics 2026, 15(5), 99; https://doi.org/10.3390/robotics15050099 (registering DOI) - 12 May 2026

Abstract

In recent years, hand exoskeleton robots have attracted extensive attention from researchers and practitioners due to their potential to rehabilitate, assist, and enhance hand movements, particularly for stroke patients. With an ageing population increasingly affected by strokes, there is a growing demand for [...] Read more.

In recent years, hand exoskeleton robots have attracted extensive attention from researchers and practitioners due to their potential to rehabilitate, assist, and enhance hand movements, particularly for stroke patients. With an ageing population increasingly affected by strokes, there is a growing demand for patient-centred interventions which place less demand on clinicians, especially wearable devices that can enhance hand function. Advances in artificial intelligence have opened new avenues for developing more reliable and adaptive assistive systems. This study presents a systematic literature review, following the PRISMA protocol on the design elements of hand exoskeleton robots, acknowledging the emerging perspectives on AI integration and ethical considerations. The study provides a comprehensive foundation for future research and development in rehabilitation technologies by systematically synthesising the current mechanical architecture, actuation, sensors, material, weight, and cost aspects of soft hand exoskeleton robots for rehabilitation. The results show important patterns and trade-offs in various design dimensions, providing useful information to direct the development of more accessible and efficient rehabilitation solutions in the future. Full article

(This article belongs to the Topic Bio-Inspired, Biomedical, Surgical, Social and AI-Integrated Bio-Mechanical Robotics)

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17 pages, 2386 KB  

Open AccessArticle

Comparative Evaluation of Deep Learning Models for Respiratory Rate Estimation Using PPG-Derived Numerical Features

by Syed Mahedi Hasan, Mercy Golda Sam Raj and Kunal Mitra

Electronics 2026, 15(5), 1108; https://doi.org/10.3390/electronics15051108 - 7 Mar 2026

Viewed by 510

Abstract

Respiratory rate (RR) is a critical vital sign for the early detection of hypoxia and respiratory deterioration, yet its continuous monitoring remains challenging in clinical environments. Photoplethysmography (PPG) provides a non-invasive source of physiological information from which respiratory dynamics can be inferred. In [...] Read more.

Respiratory rate (RR) is a critical vital sign for the early detection of hypoxia and respiratory deterioration, yet its continuous monitoring remains challenging in clinical environments. Photoplethysmography (PPG) provides a non-invasive source of physiological information from which respiratory dynamics can be inferred. In this study, numerical physiological features derived from PPG data were used to comparatively evaluate multiple deep learning models for respiratory rate estimation. Fixed-length sliding windows were constructed from the dataset and used to train five neural network architectures: a Deep Feedforward Neural Network (DFNN), unidirectional and bidirectional Recurrent Neural Networks (RNN, Bi-RNN), and unidirectional and bidirectional Long Short-Term Memory networks (LSTM, Bi-LSTM). Model performance was assessed using mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R²), and computational runtime. Results indicate that models incorporating temporal dependencies outperform the static feedforward baseline, achieving MAE values as low as 0.521 breaths/min, making them competitive with or lower than previously reported PPG-based approaches. These findings highlight the effectiveness of temporal deep learning models for respiratory rate estimation from PPG-derived numerical features and provide insight into accuracy–efficiency trade-offs relevant to real-time monitoring applications. Full article

(This article belongs to the Topic Bio-Inspired, Biomedical, Surgical, Social and AI-Integrated Bio-Mechanical Robotics)

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24 pages, 7600 KB  

Open AccessArticle

Integrated Study of Morphology and Viscoelastic Properties in the MG-63 Cancer Cell Line

by Guadalupe Vázquez-Cisneros, Daniel F. Zambrano-Gutierrez, Grecia C. Duque-Gimenez, Alejandro Flores-Mayorga, Diana G. Zárate-Triviño, Cristina Rodríguez-Padilla, Marco A. Bedolla, Jorge Luis Menchaca, Juan Gabriel Avina-Cervantes and Maricela Rodríguez-Nieto

Technologies 2026, 14(1), 60; https://doi.org/10.3390/technologies14010060 - 14 Jan 2026

Viewed by 697

Abstract

Cell morphology and its mechanical properties are crucial factors in cancer development, affecting migration, invasiveness, and the potential risk of metastasis. However, most studies address these aspects separately, limiting the understanding of how morphological complexity relates to cellular mechanics. This work presents an [...] Read more.

Cell morphology and its mechanical properties are crucial factors in cancer development, affecting migration, invasiveness, and the potential risk of metastasis. However, most studies address these aspects separately, limiting the understanding of how morphological complexity relates to cellular mechanics. This work presents an integrated approach that simultaneously quantifies morphology and viscoelasticity in the human osteosarcoma cell line MG-63. Stress–relaxation experiments and optical imaging of the same cells were performed using a custom-built system that couples Atomic Force Microscopy (AFM) with an inverted optical microscope. Morphometric parameters were extracted from cell contours, while viscoelastic properties were obtained by fitting AFM data to the Fractional Kelvin (FK) and Fractional Zener (FZ) models. Among the morphological descriptors, the Shape Complexity (SC) was proposed. It is derived from the Lobe Contribution Elliptical Fourier Analysis (LOCO-EFA), which captures fine-scale contour features overlooked by conventional metrics. Experimental results show that, in MG-63 cells, higher SC values are associated with greater stiffness, indicating a correlation between cell shape complexity and cell stiffness. Furthermore, loading-rate analysis shows that the FZ model captures strain-rate-dependent stiffening more effectively than the FK model. This methodology provides a first approach to jointly analyzing quantitative morphological parameters and mechanical properties, underlining the importance of combined studies to achieve a comprehensive understanding of cell behavior. Full article

(This article belongs to the Topic Bio-Inspired, Biomedical, Surgical, Social and AI-Integrated Bio-Mechanical Robotics)

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23 pages, 8014 KB  

Open AccessArticle

Design Evolution and Experimental Validation of the AlmatyExoElbow Assisting Device

by Dauren Bizhanov, Marco Ceccarelli, Kassymbek Ozhikenov and Nursultan Zhetenbayev

Robotics 2026, 15(1), 12; https://doi.org/10.3390/robotics15010012 - 30 Dec 2025

Cited by 1 | Viewed by 831

Abstract

This paper presents the design, prototype, and experimental evaluation of the AlmatyExoElbow, a lightweight cable-driven robotic exoskeleton that is intended to support elbow joint rehabilitation. The device provides two active degrees of freedom for flexion/extension and pronation/supination. It also incorporates a sensor-based control [...] Read more.

This paper presents the design, prototype, and experimental evaluation of the AlmatyExoElbow, a lightweight cable-driven robotic exoskeleton that is intended to support elbow joint rehabilitation. The device provides two active degrees of freedom for flexion/extension and pronation/supination. It also incorporates a sensor-based control system for accurate motion tracking. The mechanical structure is fabricated using 3D-printed PLA plastic, resulting in a compact, modular, and comfortable design suitable for prolonged use. The control architecture is based on an Arduino Nano microcontroller integrated with IMU sensors, enabling the real-time monitoring of elbow motion and the precise reproduction of physiologically relevant movement patterns. The results of experimental testing demonstrate smooth and stable operation, confirming reliable torque transmission through antagonistic cable mechanisms. Overall, the proposed design achieves a balanced combination of functionality, portability, and user comfort, highlighting its potential for upper-limb rehabilitation applications in both clinical and home-based settings. Full article

(This article belongs to the Topic Bio-Inspired, Biomedical, Surgical, Social and AI-Integrated Bio-Mechanical Robotics)

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20 pages, 22246 KB  

Open AccessArticle

Design and Evaluation of a Dual-Bendable, Compressible Robotic Guide Sheath for Heart Valve Interventions

by Matteo Arena, Weizhao Wang, Carlo Saija, Zhouyang Xu, Aya Mutaz Zeidan, Yixuan Zheng, Richard James Housden and Kawal Rhode

Robotics 2025, 14(11), 162; https://doi.org/10.3390/robotics14110162 - 3 Nov 2025

Viewed by 1375

Abstract

Structural heart interventions require precise navigation through tortuous and dynamic cardiac anatomies. However, current guide sheaths often lack sufficient maneuverability for positioning additional catheters. To address these limitations, this paper presents the design and evaluation of a robotic guide sheath with a dual-bendable, [...] Read more.

Structural heart interventions require precise navigation through tortuous and dynamic cardiac anatomies. However, current guide sheaths often lack sufficient maneuverability for positioning additional catheters. To address these limitations, this paper presents the design and evaluation of a robotic guide sheath with a dual-bendable, compressible tip. The sheath is capable of navigating complex cardiac anatomies for multiple valve interventions. The system consists of a soft continuum sheath tip driven by tendons, a laser-cut compact motorized actuation bed, and a joystick-controlled tendon actuation mechanism. A constant-curvature kinematic model maps actuation inputs to tip bending in 3D, while a custom software interface enables real-time control. Mechanical evaluation (tension, maximum bending, and contraction tests) demonstrated low actuation tension requirements (0.78 N), a wide bending range (from 80° to 90°), and promising tip compressibility (average 5 mm). Trajectory-following tests showed good accuracy, with an average error of 3.34 mm. Catheter guidance trials further validated the sheath’s ability to navigate to the right atrium and guide additional catheters effectively. This work presents a proof-of-concept robotic guide sheath with enhanced maneuverability and adaptability, establishing a foundation for future integration of sensing, automation, and clinical applications. Full article

(This article belongs to the Topic Bio-Inspired, Biomedical, Surgical, Social and AI-Integrated Bio-Mechanical Robotics)

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