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Item type:Publication, Implementation of an Embedded Electronic Control System for a Functional Prosthetic Hand(Institute of Electrical and Electronics Engineers Inc., 2025)The advancement of electronics enables the development of more complex functional prosthetics with more capabilities and functionality. The prosthetics currently developed do not leave the laboratory environment due to their complexity or the requirement to operate in conjunction with laboratory computing equipment. Therefore, it is essential to develop prosthetics that can operate outside of the laboratory environment while maintaining maximum functionality. Consequently, an electronic control system is developed that can be embedded in a functional prosthetic hand, which is then trained in real-time through electromyography (EMG) using a classification algorithm based on machine learning. The system allows the prosthetic hand to assume six different positions and be controlled by electromyography, recognizing flexion and extension movements of the wrist. This results in a control system that can be fully embedded in the prosthetic hand, with an appropriate weight and size to maintain the aesthetics and functionality of the prosthesis.3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Affordable AI-Driven and 3D-Printed Personalized Myoelectric Prosthesis: Design, Development, and Assessment(Institute of Electrical and Electronics Engineers Inc., 2025)Upper-limb amputations significantly affect independence and quality of life, particularly in low-income regions where advanced prosthetic technology is costly and lacks adequate personalization. Conventional myoelectric prostheses, while offering functional restoration, have limited adaptability and high cost. This study presents a personalized transradial myoelectric prosthesis that combines additive manufacturing and Artificial Intelligence (AI) control, offering an accessible and high-performance solution. The prosthesis design utilizes additive manufacturing (3D printing) for anatomical personalization via 3D scanning and parametric modeling. An AI-driven control system utilizes machine learning to classify electromyography (EMG) signals in real-time, specifically detecting the user’s intention to perform flexion or extension movements, and tailoring responses to individual users. Evaluation employed the "Brief Activity Measure for Upper Limb Amputees (BAM-ULA)" protocol with nine participants with transradial amputations. Trials with the nine participants yielded an average BAM-ULA score of 7.4 out of 10 (Standard Deviation (SD) 0.7). This demonstrated robust functional performance, comparable to high-end commercial devices in initial tests. Gross motor tasks saw 100% success rates; fine motor tasks, 22.2%. Integrating AI and additive manufacturing resulted in an affordable, high-performance, personalized prosthesis. This work highlights how localized digital manufacturing enables accessible customization for users in low-resource settings. The main novelty is this validated integration of personalized additive manufacturing and adaptive AI control in an affordable transradial prosthesis addressing the needs of developing countries.3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multimodal biomechanical dataset from transtibial amputees and able-bodied adults across five locomotion tasks(Nature Research, 2026)This dataset addresses the need for multimodal biomechanical recordings during over-ground walking, ramps, and stairs by synchronously capturing electromyographic (EMG), inertial (IMU), and plantar pressure data. We collected data from 45 adults (15 with unilateral transtibial amputation and 30 without amputation) who completed five standardized locomotor tasks: level walking, ramp ascent/descent, and stair ascent/descent. Each participant performed 50 supervised trials. Wireless EMG and IMU sensors (Delsys Trigno Avanti) measured muscle activation and kinematics, while intelligent insoles (XSENSOR) captured plantar pressure distribution. Raw data were saved in.hpf (EMG/IMU) and.XSN (pressure) formats, with processed outputs in.csv files. All data are organized by task and sensor type, including complete participant metadata. Key dataset outputs include time-normalized EMG amplitudes, segment kinematics, and pressure maps across terrains and populations. The dataset was validated technically and experimentally during the acquisition. This resource enables quantitative analysis of gait adaptation and supports machine learning for locomotion classification. Data are provided in accessible formats to foster reuse in biomechanics, rehabilitation engineering, robotics, and clinical gait research.7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Assessing Performance and Mechanics of the PUCP IArm Upper Limb Prosthesis(Springer Science and Business Media Deutschland GmbH, 2025)Access to upper limb prostheses in developing countries, including Peru, is constrained. Models with features like increased individual finger strength and greater degrees of freedom tend to be costly, compelling individuals to opt for less advanced passive prostheses or aesthetic options. This creates a significant necessity for affordable prosthetics that do not compromise essential functionality and allow users to perform daily activities effectively. An example of this development is the PUCP IArm prosthesis, a laboratory model designed and developed at the “Pontificia Universidad Católica del Perú” (PUCP, for its initials in Spanish). This functional and accessible prosthesis is manufactured using additive manufacturing technologies. This study aims to characterize the mechatronic properties and evaluate the functionality of the hand component of the PUCP IArm prosthesis. Evaluations included videogrammetric assessments, finger force measurements, distal tensile tests, static downward bending tests, and response latency measurements between gesture detection and actuation. The assessed prosthesis weighs 230 g and has a maximum load capacity of 1.83 N in pinch gestures and 19.05 N in cylindrical grip. Compared with myoelectric activation prosthesis models developed using digital fabrication techniques, the results obtained by the PUCP IArm prosthesis are promising and stand out.1