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Low-cost sensors provide a unique opportunity to continuously monitor patient progress during rehabilitation; however, these sensors have yet to demonstrate the fidelity and lack the calibration paradigms necessary to be viable tools for clinical research. The purpose of this study was to validate a low-cost wearable sensor that accurately measured peak knee extension during clinical exercises and needed no additional equipment for calibration. Sagittal plane knee motion was quantified using a 9-axis motion sensor and directly compared to motion capture data. The motion sensor measured the field strength of a strong earth magnet secured to the distal femur, which was correlated with knee angle during a simple calibration process. Peak knee motions and kinematic patterns were compared with motion capture data using paired t-tests and cross correlation, respectively. Peak extension values during seated knee extensions were accurate within 5 degrees across all subjects (root mean square error: 2.6 degrees, P = 0.29). Knee flexion during gait strongly correlated (0.84 ≤ r ≤ 0.99) with motion capture measurements but demonstrated peak flexion errors of 10 degrees. In this study, we present a low-cost sensor (≈$ 35 US) that accurately determines knee extension angle following a calibration procedure that did not require any other equipment. Our findings demonstrate that this sensor paradigm is a feasible tool to monitor patient progress throughout physical therapy. However, dynamic motions that are associated with soft-tissue artifact may limit the accuracy of this type of wearable sensor.
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http://dx.doi.org/10.1016/j.jbiomech.2019.04.003 | DOI Listing |
J Neuroeng Rehabil
September 2025
Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tübingen, 72076, Tübingen, Germany.
Innovative technology allows for personalization of stimulation frequency in dual-site deep brain stimulation (DBS), offering promise for challenging symptoms in advanced Parkinson's disease (PD), particularly freezing of gait (FoG). Early results suggest that combining standard subthalamic nucleus (STN) stimulation with substantia nigra pars reticulata (SNr) stimulation may improve FoG outcomes. However, patient response and the optimal SNr stimulation frequency vary.
View Article and Find Full Text PDFSci Rep
September 2025
Department of Communications and Electronics, Delta University for Science and Technology, Mansoura, Egypt.
J Colloid Interface Sci
September 2025
Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, College of Engineering, Zhejiang Normal University, Jinhua 321004, China. Electronic address:
Developing high-performance wearable flexible sensors that can adapt well to complex environments has become a hotspot. Herein, a polyvinyl alcohol based composite hydrogel sensor with high mechanical strength, desirable frost/swelling resistance, and highly sensitive sensing performance was proposed by a multi-component collaborative design strategy. Meanwhile, an intelligent gesture recognition system was established by combining machine learning algorithm.
View Article and Find Full Text PDFEmerg Top Life Sci
September 2025
Hurdle.bio / Chronomics Ltd., London, UK.
Artificial intelligence (AI) is transforming many fields, including healthcare and medicine. In biomarker discovery, AI algorithms have had a profound impact, thanks to their ability to derive insights from complex high-dimensional datasets and integrate multi-modal datatypes (such as omics, electronic health records, imaging or sensor and wearable data). However, despite the proliferation of AI-powered biomarkers, significant hurdles still remain in translating them to the clinic and driving adoption, including lack of population diversity, difficulties accessing harmonised data, costly and time-consuming clinical studies, evolving AI regulatory frameworks and absence of scalable diagnostic infrastructure.
View Article and Find Full Text PDFNanomicro Lett
September 2025
Nanomaterials & System Lab, Major of Mechatronics Engineering, Faculty of Applied Energy System, Jeju National University, Jeju, 63243, Republic of Korea.
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring, clinical diagnosis, and robotic applications. Nevertheless, it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility, adhesion, self-healing, and environmental robustness with excellent sensing metrics. Herein, we report a multifunctional, anti-freezing, self-adhesive, and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes (CoN CNT) embedded in a polyvinyl alcohol-gelatin (PVA/GLE) matrix.
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