Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment

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Items 101–181 of 181

1. „Wearables“ in der Behandlung neurologischer Erkrankungen – wo stehen wir heute?
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2. Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization
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3. Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson’s disease patients
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4. Automated Classification of Postural Control for Individuals With Parkinson’s Disease Using a Machine Learning Approach: A Preliminary Study
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5. A Framework for Interpretable Full-Body Kinematic Description Using Geometric and Functional Analysis
   Boulbaba Ben Amor, Anuj Srivastava, Pavan K. Turaga, Grisha Coleman · 2019 · IEEE Transactions on Biomedical Engineering
6. Estimating motor symptom presence and severity in Parkinson’s disease from wrist accelerometer time series using ROCKET and InceptionTime
   Cedric Donié, Neha Das, Satoshi Endo, Sandra Hirche · 2025 · Scientific Reports
7. Detecting Parkinson’s Disease with Convolutional Neural Networks: Voice Analysis and Deep Learning
   Kevin Saltos, Luis Zhinín-Vera, Cristina Godoy, Roberth Chachalo, Diego A. Almeida-Galárraga, Carolina Cadena-Morejón, Andrés Tirado-Espín, Jonathan Cruz-Varela, Fernando Villalba-Meneses · 2023 · Communications in computer and information science
8. Open-source data management system for Parkinson’s disease follow-up
   João Paulo Folador, Marcus Fraga Vieira, Adriano Alves Pereira, Adriano de Oliveira Andrade · 2021 · PeerJ Computer Science
9. IoT-based location-aware smart healthcare framework with user mobility support in normal and emergency scenario: a comprehensive survey
   Riddhi Kumari Bhadoria, Jayita Saha, Suparna Biswas, Chandreyee Chowdhury · 2020 · Elsevier eBooks
10. Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?
    You Li, Ruizhi Chen, Xiaoji Niu, Yuan Zhuang, Zhouzheng Gao, Xin Hu, Naser El‐Sheimy · 2020 · arXiv (Cornell University)
11. IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A Review
    Fan Bo, Mustafa Ozkan Yerebakan, Yanning Dai, Weibing Wang, Jia Li, Shuo Gao, Hu Boyi · 2022
12. Machine Learning for Parkinson’s Disease and Related Disorders
    Johann Faouzi, Olivier Colliot, Jean‐Christophe Corvol · 2023 · Neuromethods
13. Highly-accurate binary tiny neural network for low-power human activity recognition
    Antonio De Vita, Danilo Pietro Pau, Luigi Di Benedetto, Alfredo Rubino, Frédéric Pétrot, Gian Domenico Licciardo · 2021 · Microprocessors and Microsystems
14. The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems
    Giorgio de Alteriis, Davide Ruggiero, Francesco Del Prete, Claudia Conte, Enzo Caputo, Verdiana Bottino, Filippo Carone Fabiani, Domenico Accardo, Rosario Schiano Lo Moriello · 2023 · Sensors
15. Parkinson's Disease Prediction Using Machine Learning Techniques
    M. Harry, V. Vijula, J Pearly Princess · 2024 · IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS)
16. Predicting Neurodegenerative Diseases Using a Novel Blood Biomarkers-based Model by Machine Learning
    Shu-I Chiu, Chin‐Hsien Lin, Wee Shin Lim, Ming‐Jang Chiu, Ta‐Fu Chen, Jyh‐Shing Roger Jang · 2019 · Conference on Technologies and Applications of Artificial Intelligence (TAAI)
17. Deep learning approaches for human activity recognition using wearable technology
    Milica M. Janković, Andrej M. Savić, Marija M. Novicic, Mirjana Popović · 2018 · Medicinski podmladak
18. Audio-Based Neural Network to Classify Patients With Early Parkinson’s Disease
    Weikang Hou, Changqin Quan, Zhonglue Chen, Sheng Cao, Kang Ren, Wen Su, Zhiwei Luo · 2025 · IEEE Access
19. Attention-Based Deep Learning Model for Early Detection of Parkinson's Disease
    Mohd Sadiq, Mohd Tauheed Khan, Sarfaraz Masood · 2022 · Computers, materials & continua/Computers, materials & continua (Print)
20. A multimodal screening system for elderly neurological diseases based on deep learning
    Sang‐Young Park, Changho No, Sora Kim, Kyoungmin Han, Jin‐Man Jung, Kyum‐Yil Kwon, Minsik Lee · 2023 · Scientific Reports
21. A Patient Oriented Framework using Big Data & C-means Clustering for Biomedical Engineering Applications
    Md Mahbub Mishu · 2019 · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)
22. A novel variational mode decomposition based convolutional neural network for the identification of freezing of gait intervals for patients with Parkinson's disease
    Mohamed Shaban · 2024 · Machine Learning with Applications
23. Quantitative evaluation of hand functions using a wearable glove with multiple sensors
    Chaoyue Yin, Quan Liu, Wei Meng, Qingsong Ai · 2021 · IEEE International Conference on Real-time Computing and Robotics (RCAR)
24. An insight on recent advancements and future perspectives in detection techniques of Parkinson’s disease
    Snehith Sankineni, Aanchal Saraswat, M. Suchetha, Sathyanarayanan N. Aakur, S. Sehastrajit, D. Edwin Dhas · 2023 · Evolutionary Intelligence
25. Tremor Identification Using Machine Learning in Parkinson's Disease
    Angana Saikia, Vinayak Majhi, Masaraf Hussain, Sudip Paul, Amitava Datta · 2019 · Advances in medical technologies and clinical practice book series
26. Increased Temporal Variability of Gait in ASD: A Motion Capture and Machine Learning Analysis
    Katharine Goldthorp, Benn Henderson, Pratheepan Yogarajah, Bryan Gardiner, T.M. McGinnity, Brad Nicholas, Dawn C. Wimpory · 2025 · Biology
27. Enhancing Parkinson's Disease Diagnosis Through Mayfly-Optimized CNN BiGRU Classification
    Hariharan Dhanaskaran, Dhaya Chinnathambi, Srivel Ravi, Viswanathan Dhandapani, M. V. Ramana Rao, Mohammed Abdul Matheen · 2024 · Advances in medical technologies and clinical practice book series
28. Towards QoE Management for Post-Pandemic Online Learning : Invited Paper
    Truong Cong Thang, Yutaka Watanobe, Rage Uday Kiran, Incheon Paik · 2022 · International Conference on Knowledge and Systems Engineering/Proceedings
29. Decoding Artificial Intelligence in Neuroscience: Applications Beyond Diagnosis
    Sagar Salave, Dhwani Rana, Derajram M. Benival, Aakanchha Jain · 2023 · Current Indian Science
30. Time-resolved quantification of fine hand movements as a proxy for evaluating bradykinesia-induced motor dysfunction
    Rachel K. Spooner, Bahne H. Bahners, Alfons Schnitzler, Esther Florin · 2024 · Scientific Reports
31. Review of Parkinson’s disease detection with deep‑learning machines
    Ali Abdulameer Aldujaili, Manuel Rosa-Zurera, Manuel Utrilla-Manso · 2024 · Multimedia Tools and Applications
32. Parkinson Hastalığı Tespitinde Farklı Boyutsallık İndirgeme Yöntemlerinin Karşılaştırılması
    Hakan Gündüz · 2019 · European Journal of Science and Technology
33. WITHDRAWN: A technical survey on various machine learning approaches for Parkinson’s disease classification
    B. Sabeena, S. Sivakumari, P. Amudha · 2020 · Materials Today Proceedings · Retracted
34. IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A Review
    Fan Bo, Mustafa Ozkan Yerebakan, Yanning Dai, Weibing Wang, Jia Li, Shuo Gao, Hu Boyi · 2022
35. aiGeneR 3.0: an enhanced deep network model for resistant strain identification and multi-drug resistance prediction in Escherichia coli causing urinary tract infection using next-generation sequencing data
    Debasish Swapnesh Kumar Nayak, Abhilash Pati, Amrutanshu Panigrahi, Mudassir Khan, Bayan Ibrahimm Alabdullah, Santanu Kumar Sahoo, Bibhuprasad Sahu, Abrar Almjally, Saurav Mallik, Tripti Swarnkar · 2025 · Frontiers in Genetics
36. Optimized Parameter Selection for Parkinson's Diagnosis Using a Hybrid Machine Learning Models
    Husen Lakdawala, Zuzer Chathiwala, Yug Trivedi, Jaikee Kumar Singh, Himanshu Avashthi, Sejal Shah · 2025 · IEEE Region Humanitarian Technology Conference
37. A Hybrid Approach to Parkinson's Disease Detection using Speech Attributes: The Combination of SMOTE and Active Learning
    Naga Tejaswi Mamidi, Gnana Flarance Sravanthi Medikonda, Sai SanghaviValmeti, Vijaya Kumar Vadladi · 2023 · International Conference on Applied Artificial Intelligence and Computing (ICAAIC)
38. Motor assessment of X-linked dystonia parkinsonism via machine-learning-based analysis of wearable sensor data
    Federico Parisi, Giulia Corniani, Paolo Bonato, David Balkwill, Patrick J. Acuña, Criscely L. Go, Nutan Sharma, Christopher D. Stephen · 2024 · Scientific Reports
39. Detection of Parkinson’s Disease Using Non-invasive Biomarkers
    Soundarya Ganesh, Surabhi Vedagiri, K. S. Srinivas · 2024 · Lecture notes in networks and systems
40. A Convolutional Neural Network Approach for Quantification of Tremor Severity in Neurological Movement Disorders
    Rajesh Ranjan, Braj Bhushan, Marimuthu Swami Palaniswami, Alok Verma · 2020 · Advances in intelligent systems and computing
41. Comparative Study of Different Pre-Trained Deep Learning Models for Footwear Classification
    Chandu Boppana, Naga Amrutha Chituri, Vamsi Pallapu, Bala Vamsi Boyapati, Venkatrama Phani Kumar, Venkata Krishna Kishore Kolli · 2025 · International Conference on Intelligent Systems and Computational Networks (ICISCN)
42. Objective assessments of human motor ability of the upper limb: A systematic review
    Edwin Duque, Helmuth Trefftz, Sakti Srivastava · 2020 · Technology and Disability
43. A pilot study for testing a low-cost 3D design with an inertial sensor for the quantitative assessment of finger tapping in patients with Parkinson’s Disease
    Stefano E. Romero, Katherin Zumaeta, Midori Sanchez, Estiven Torres, Karlo J. Lizárraga, Benjamín Castañeda · 2023 · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
44. Learning Spatio-Temporal Radon Footprints for Assessment of Parkinson’s Dyskinesia
    Paraskevi-Antonia Theofilou, Georgios Tsatiris, Stefanos Kollias · 2024 · Electronics
45. Towards early detection of chronic kidney disease based on gait patterns: IMU-based approach using neural networks
    Sunghan Lee, Jeonghwan Koh, GyeongBong Kim, Seokjue Jeong, Young‐Jin Song, Moo‐Eob Ahn, Hyunsuk Kim, In Cheol Jeong · 2024 · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
46. A New Geometric Algebra-Based Classification of Hand Bradykinesia in Parkinson’s Disease Measured Using a Sensory Glove
    Giovanni Saggio, Paolo Roselli, Luca Pietrosanti, Alessandro Romano, Nicola Arangino, Martina Patera, Antonio Suppa · 2025 · Algorithms
47. Improved Usability of IOT Devices in Healthcare Using Big Data Analysis
    V. Kakulapati, Mahender Reddy S. · 2018 · Advances in computational intelligence and robotics book series
48. Um Modelo de Predição de Mortalidade em Unidades de Terapia Intensiva Baseado em Deep Learning
    Diogo Schmidt, Denise Bandeira da Silva, Cristiano André da Costa, Rodrigo da Rosa Righi · 2018
49. Supination/pronation movement quantification using stereoscopic vision based system towards Parkinson’s Disease assessment – A pilot study
    Pedro Guilherme Vaz, Ana Luísa dos Santos Reis, J. M. R. Cardoso · 2020 · Biomedical Signal Processing and Control
50. Detección automática de síntomas motores asociados a la enfermedad de Parkinson mediante relojes y teléfonos móviles inteligentes aplicando técnicas de inteligencia artificial
    Luis Sigcha · 2021
51. An Approach to Detect Parkinson’s Disease Employing Pre-trained Deep Neural Network on Hypokinetic Dysarthria Symptoms
    Samiappan Dhanalakshmi, Harisudha Kuresan, Arathy Jeevan, Sukirti Gupta · 2022 · Research Square
52. Comparative Analysis of Conventional Machine Learning and Deep Learning Techniques for Predicting Parkinson's Disease
    Monika Sethi, Vidhu Baggan · 2025
53. A comprehensive study in assembling deep convolutional neural networks for image classification
    Rom Uddamvathanak, Feng Yang · 2019 · International Conference on Signal Processing Systems (ICSPS)
54. Continuous Parkinsonian Tremor Estimation Using Motion Data
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55. Researcher Framework Using MongoDB and FCM Clustering for Prediction of the Future of Patients from EHR
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56. Peer Review #1 of "Open-source data management system for Parkinson’s disease follow-up (v0.1)"
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57. Sistema open-source para gerenciamento de dados da doença de Parkinson e detecção do sintoma do tremor em desenhos manuscritos pelo uso de histogramas de gradientes orientados
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58. Sensor Approach for Brain Pathophysiology of Freezing of Gait in Parkinson's Disease Patients
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59. Ttechnological advances in deep brain stimulation : towards an adaptive therapy
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60. Peer Review #3 of "Open-source data management system for Parkinson’s disease follow-up (v0.2)"
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61. Peer Review #2 of "Open-source data management system for Parkinson’s disease follow-up (v0.1)"
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62. Tremor Identification Using Machine Learning in Parkinson's Disease
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63. Peer Review #2 of "Open-source data management system for Parkinson’s disease follow-up (v0.2)"
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64. Design and Development of Efficient Model to Predict Neurodegenerative Disorders Using Advanced LSTM: A Review of Literature
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65. Effective Diagnosis of Parkinson’s Disease Using Machine Learning Techniques
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66. Semi-supervised Co-teaching for Monitoring Motor States of Parkinson’s Disease Patients
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67. Parkinson’s Disease Verdict and Cataloguing by Using Various Machine Learning and Deep Learning Techniques: A Technical Review
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68. Research hotspots and future trends of insomnia in Parkinson’s disease: a bibliometric and visualization analysis from 1973 to 2024
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69. A novel computational intelligence model using improvised feature selection for Parkinson’s disease classification
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70. Severity Prediction of Parkinson’s Disease with Machine Learning and Explainable AI
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71. Feature Optimization and Stacked Ensemble Learning for Parkinson’s Disease Classification Using Speech Analysis
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72. Reducing robotic upper-limb assessment time while maintaining precision: a time series foundation model approach
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73. Analysis of Electromyography Wearable Sensors and Deep Learning Algorithms used in Parkinson's Diagnosis
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74. Federated Learning for Early Parkinson's Diagnosis: Deep Learning with Vocal Frequency Features
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75. Anomaly characterization in the MRI data of 'de novo' Parkinson patients
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76. Les algorithmes et la robotique permettent de décrire comment nous apprenons l'écriture manuscrite, et comment mieux aider les enfants avec des difficultés dans ce domaine
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77. Individual Risky Action Warning of Wearable Sensors on Personalized Health Profiles via LLM
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78. Exploring Machine Learning in Voice‑Based Parkinson's Disease Diagnosis
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79. Advancing Neurological Diagnosis: Ensemble Deep Learning Approach for Parkinson’s Disease Diagnosis
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80. Neurological Disorder Detection Using Wearable Sensors
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81. Machine learning-integrated wearable sensors for intelligent disease diagnosis and personalized healthcare
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