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Item type:Publication, Smartphone-Based System for Quantifying Tremor and Freezing of Gait in Parkinson's Disease(Institute of Electrical and Electronics Engineers Inc., 2025-01-01)The primary objective of this study was to develop a smartphone-based system for the quantitative assessment of motor symptoms in Parkinson’s disease (PD), with a focus on tremor and freezing of gait (FOG). The system employed inertial sensors embedded in iOS devices to capture motion data at 50 Hz and integrated signal processing with machine learning techniques for symptom quantification. For FOG assessment, a Random Forest classifier achieved 90% accuracy using time- and frequency-domain features. Tremor analysis relied on spectral methods to identify characteristic oscillations within the 4–6 Hz range typically associated with PD. Technical validation with five healthy control subjects demonstrated consistent system performance, supporting its applicability in clinical settings. All controls exhibited a Freezing Index (FI < 2.5) and a Tremor Index (TI < 25%). These findings highlight the potential of the proposed system as a portable solution for the objective monitoring of Parkinson’s disease symptoms, complementing clinical evaluations and supporting personalized therapeutic approaches.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Real-Time EMG Classification for Bipedal Robot Control: A Hands-On Educational Platform(Institute of Electrical and Electronics Engineers Inc., 2025-01-01)A hands-on educational platform is presented for teaching biosignal processing, machine learning, and robotics through real-time control of a bipedal robot using electromyo-graphic (EMG) signals. Lower-limb EMG signals are captured using Delsys sensors and processed on a Raspberry Pi, where relevant features are extracted and classified using a K-Nearest Neighbors (KNN) algorithm. The recognized movements are then mapped to motor commands and transmitted to an Arduino-based controller that actuates the bipedal robot. The system is designed to create a demonstrative module for hands-on learning in biomedical engineering and robotics education. Experimental results demonstrate reliable physical execution of learned movements with the implemented algorithm. By integrating signal acquisition, real-time classification, and robotic control, this platform provides a practical and engaging approach for students to explore human-machine interfaces.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Parkinson's Hand Tremor Simulator with a Double Crank-Crank Mechanism to Measure the Essential Frequency(Springer Science and Business Media Deutschland GmbH, 2025-01-01)This article introduces the concept of penal extractivism in the punishment and society literature. We define penal extractivism as the punitive strategies that a state implements to safeguard extractive industries from citizens' contention. This concept2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimized Child-Centered Textile Design for a Soft Robotic Glove in Pediatric Rehabilitation(Faculty of Engineering, Universitas Indonesia, 2025-09-22)Cerebral palsy, a neurological condition that can affect the mobility and coordination of the upper limbs, presents challenges for the design of functional, comfortable, and emotionally acceptable rehabilitation devices to enhance therapeutic adherence in children. This study presents the development and optimization of a soft robotic glove specifically designed for the rehabilitation of children with cerebral palsy, integrating ergonomics, functionality, and esthetics. The multilayer design of the glove prioritizes ergonomics through the use of three spandex materials: Dry Spandex, Fluity, and Lenatex, selected for their flexibility and anatomical fit. Mechanical tests conducted according to the American Society of Testing and Materials (ASTM) 4964-96 standards evaluated the behavior of the spandexes after three usage cycles. Dry Spandex, which was used in the inner layer, showed an average tension decay of 4%, which is notable for its high elasticity and uniform tension decay at low elongations. The outer layer of Fluity exhibited a higher tension decay of up to 7.7% but provided a balance between comfort and adequate initial support for electronic and pneumatic components. Lenatex, the most rigid spandex with the lowest variance (0.8), was used in the wristband, providing a uniform and predictable mechanical response. Additionally, a usability survey with children revealed positive perceptions of the glove’s esthetics, instructions, and overall experience, highlighting areas for improvement. The findings related to technical and emotional features support the design’s potential to achieve a balance between comfort and usability, providing insight for future applications of soft robotics in child-centered designs.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimization of Pneumatic Finger Actuator Design for Soft Robotic Applications in Hand Therapy(Institute of Electrical and Electronics Engineers Inc., 2025-01-01)The human hand plays a fundamental role in daily activities and the development of fine motor skills, particularly during childhood; however, traumatic injuries or neurological disorders affecting hand joints can severely impair functionality and compromise the long-term quality of life in pediatric populations. Robotic gloves equipped with pneumatic actuators have emerged as promising tools to support rehabilitation in these cases, offering precise and adaptive assistance to restore movement. Despite their potential, current devices exhibit notable limitations, particularly in terms of durability and uniformity of pressure distribution in the actuators, which often result in premature material failure and reduced therapeutic effectiveness. This study aimed to optimize the design of pneumatic finger actuators for pediatric rehabilitation by evaluating how actuator geometry influences fatigue life and pressure performance. Using room temperature vulcanizing (RTV) Type 6 silicone for its favorable mechanical properties, three actuator designs were fabricated with polylactic acid (PLA) molds produced through 3D printing and subjected to fatigue, pressure, and deformation tests. The results showed that geometry had a substantial impact on performance: Design 1 withstood 3393 cycles at 62 kPa, whereas Design 2 failed after 1293 cycles at 51 kPa. Fatigue analysis confirmed that internal reinforcements and the distribution of stress concentration zones play a critical role in enhancing long-term durability.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modular Glove for Quantification of Bradykinesia and Tremor in Parkinson's Disease(Springer Science and Business Media Deutschland GmbH, 2026-01-01)Parkinson’s disease (PD) is characterized by motor symptoms, among which bradykinesia and tremor are key indicators for diagnosis and monitoring. This study presents and evaluates a modular glove incorporating inertial sensors (MPU9265) for the objective and quantitative assessment of these two symptoms. Motor tasks adapted from the Movement Disorder Society - Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), including finger tapping, pronation-supination, finger-to-nose maneuver, and resting tremor, were performed to capture parameters such as frequency, rhythm, and entropy. A pilot technical evaluation using healthy volunteers was conducted to validate the device’s functionality, followed by tests with a mechanical tremor simulator that yielded results consistent with reported findings in the literature. The data indicated a decrease in finger tapping frequency and an increase in rhythm irregularity during Parkinson’s simulations. The findings suggest that the proposed modular device could serve as a practical tool for diagnosis and continuous monitoring of PD. At the same time, further validation with real patients would be valuable to confirm its effectiveness and to refine aspects such as ergonomics and measurement accuracy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, IoMT and explainable AI-enabled wearable system for classifying tremor and motor patterns in Parkinson's disease(Elsevier B.V., 2026-03-01)The prevalence of Parkinson’s disease (PD) represents a significant global health concern due to its debilitating motor symptoms, particularly tremors. This pilot study presents an interpretable Internet of Medical Things (IoMT)-based monitoring system for analyzing hand tremors in individuals with PD. The proposed framework integrates a 6-axis inertial sensor for motion tracking, a low-power wireless microcontroller for data acquisition and connectivity, and a gradient boosting-based machine learning model (LightGBM) for tremor classification. During the data acquisition phase, signals from a triaxial accelerometer and gyroscope are sampled at 66.67 Hz, covering the characteristic 4–6 Hz Parkinsonian tremor frequency band. A RESTful API transmits the acquired data to a server over Wi-Fi, where it is stored in a relational database. Signal processing includes noise reduction, temporal segmentation, and frequency-domain feature extraction using the fast Fourier transform (FFT). The LightGBM classifier categorizes motor activity into Parkinsonian tremor, voluntary movement, or absence of tremor. Additionally, a web-based user interface supports real-time signal visualization and structured clinical data entry. As a feasibility study, the system demonstrates strong classification performance, achieving 95.64% accuracy and an F1-score of 0.95 on a dataset comprising 10,314 samples from nine participants (four with clinically diagnosed PD and five healthy controls). Although evaluated under controlled laboratory conditions, these results support the potential of the proposed system as a low-cost, interpretable tool for objective tremor assessment and continuous monitoring of Parkinson’s disease symptoms.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Human-Robot Interaction Through EMG and KNN-Based Gesture Classification(Springer Science and Business Media Deutschland GmbH, 2026-01-01)This paper presents the development and real-time deployment of an EMG-based gesture recognition system using surface electromyographic (sEMG) signals for robotic arm control. The system classifies four forearm gestures, hand extension, hand flexion, thumbs up, and fist, from sEMG signals recorded from four key muscles. Preprocessing is performed by bandpass and median filtering, followed by the extraction of five time-domain features. A k-Nearest Neighbors (k-NN) classifier is trained on a dataset collected from 35 participants; the model is trained and implemented on a Raspberry Pi embedded platform for real-time inference and gesture-to-motion mapping using the Arduino Braccio++ robotic arm. Each gesture corresponds to a distinct pose in the robotic manipulator, which is made visible to represent the user’s intention. This work reports findings from a pilot study aimed at validating the system’s technical feasibility and establishing baseline performance. This work demonstrates the feasibility of real-time EMG-based gesture control, highlighting its potential for applications in assistive and wearable robotics. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hand and Wrist Motion Simulator for the Evaluation of Robotic Training Gloves Aimed at Children(Springer Science and Business Media Deutschland GmbH, 2025-01-01)Loss of motor function in the upper extremities of children due to injuries or medical conditions requires effective and specialized therapeutic solutions. Hand and wrist rehabilitation is complex because of the smaller anatomical structure of children, as well as ethical considerations that restrict the use of invasive treatments or tests. Therefore, it is necessary to verify the efficacy and safety of rehabilitation devices in improving motor function. A 3D-printed hand and wrist simulator was developed to validate robotic gloves for 8- to 9-year-old children. This simulator effectively replicates the intricate biomechanical movements of the human hand and emulates the weight and structure of a natural hand, ensuring a realistic testing platform. The results confirm that the simulator’s range of motion aligns with human ranges, demonstrating its ability to simulate natural biomechanics. This advancement serves as a starting point for the evaluation of rehabilitation devices and research in pediatric biomechanics, while also being relevant for the design of these devices to improve the quality of life of young patients.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of a Mechanical Thumb Prosthesis for Functional Restoration in Partial Amputations(Institute of Electrical and Electronics Engineers Inc., 2025-01-01)Loss of thumb function due to partial amputations significantly affects the usability of the hand and the quality of life of users. Effective prosthetic solutions are required to restore functionality. In this study, we present the development and evaluation of a mechanical thumb prosthesis designed for people with partial amputations, based on anthropometric proportions of an average adult between 33 and 35 years old, with movements such as flexion and extension. A simulation was performed using a load of 5 N to evaluate the safety factor in the prosthesis structure. The results show a safety range between 2.991 and 15.00, which validates the resistance and robustness of the structure to the loads applied under normal conditions of use. This advancement provides users with a tool to enhance their autonomy and well-being, thereby improving their quality of life.5