scholarly article | Q13442814 |
P2093 | author name string | S Arora | |
V Venkataraman | |||
E R Dorsey | |||
M A Little | |||
S Donohue | |||
A Zhan | |||
K M Biglan | |||
P433 | issue | 6 | |
P921 | main subject | smartphone | Q22645 |
Parkinson's disease | Q11085 | ||
P1104 | number of pages | 4 | |
P304 | page(s) | 650-653 | |
P577 | publication date | 2015-03-07 | |
P1433 | published in | Parkinsonism and Related Disorders | Q15762600 |
P1476 | title | Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study | |
P478 | volume | 21 |
Q47619924 | A Solution-Processable, Omnidirectionally Stretchable, and High-Pressure-Sensitive Piezoresistive Device |
Q33449915 | A Validation Study of a Smartphone-Based Finger Tapping Application for Quantitative Assessment of Bradykinesia in Parkinson's Disease |
Q38573937 | Advances in clinical trials for movement disorders |
Q92853571 | An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors |
Q48108026 | App-Based Bradykinesia Tasks for Clinic and Home Assessment in Parkinson's Disease: Reliability and Responsiveness |
Q90285045 | Automated Detection of Parkinson's Disease Based on Multiple Types of Sustained Phonations Using Linear Discriminant Analysis and Genetically Optimized Neural Network |
Q30359061 | Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders. |
Q33790136 | Biometric Digital Health Technology for Measuring Motor Function in Parkinson's Disease: Results from a Feasibility and Patient Satisfaction Study |
Q52152086 | Body-worn sensors--the brave new world of clinical measurement? |
Q48091456 | Cognitive Rehabilitation in Parkinson's Disease: Is it Feasible? |
Q37310810 | Computer keyboard interaction as an indicator of early Parkinson's disease |
Q36391409 | Deep Brain Stimulation: Expanding Applications |
Q42370726 | Detecting Parkinson's disease from sustained phonation and speech signals. |
Q90841006 | Detecting the impact of subject characteristics on machine learning-based diagnostic applications |
Q64447841 | Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice |
Q100752793 | Early detection and tracking of bulbar changes in ALS via frequent and remote speech analysis |
Q93155315 | Environment Monitoring for Anomaly Detection System Using Smartphones |
Q52559502 | Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. |
Q47108732 | Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis |
Q47276206 | Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease. |
Q64113563 | Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study |
Q33450414 | Large-Scale Wearable Sensor Deployment in Parkinson's Patients: The Parkinson@Home Study Protocol. |
Q31120227 | Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures |
Q56518939 | Measuring nominal scale agreement among many raters |
Q40396102 | Moving Parkinson care to the home |
Q58610357 | Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-wise Missing Data |
Q92381961 | OBJECTIVE ASSESSMENT OF VOCAL TREMOR |
Q57794798 | On-Body Sensor Positions Hierarchical Classification |
Q58750345 | Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling |
Q97886732 | Outcome measures based on digital health technology sensor data: data- and patient-centric approaches |
Q47730591 | Patient-driven N-of-1 in Parkinson's Disease. Lessons Learned from a Placebo-controlled Study of the Effect of Nicotine on Dyskinesia. |
Q39050215 | Physical therapy and occupational therapy in Parkinson's disease |
Q92554788 | Predicting motor, cognitive & functional impairment in Parkinson's |
Q50103903 | Predictors of enrollment in individual- and couple-based lifestyle intervention trials for cancer survivors |
Q64956232 | Quantitative Measurement of Akinesia in Parkinson's Disease. |
Q64767666 | Robust Detection of Parkinson's Disease Using Harvested Smartphone Voice Data: A Telemedicine Approach |
Q57459510 | Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD |
Q42334426 | Smartphones as new tools in the management and understanding of Parkinson's disease |
Q38768719 | Speech disorders in Parkinson's disease: early diagnostics and effects of medication and brain stimulation |
Q52620035 | Supervised versus unsupervised technology-based levodopa monitoring in Parkinson's disease: an intrasubject comparison. |
Q89870391 | Tablet-Based Application for Objective Measurement of Motor Fluctuations in Parkinson Disease |
Q37635629 | Technologies Assessing Limb Bradykinesia in Parkinson's Disease. |
Q38820246 | Technology in Parkinson's disease: Challenges and opportunities |
Q88563765 | Telehealth Management of Parkinson's Disease Using Wearable Sensors: An Exploratory Study |
Q39915655 | Telehealth for patients with Parkinson's disease: delivering efficient and sustainable long-term care |
Q89870358 | The First Frontier: Digital Biomarkers for Neurodegenerative Disorders |
Q33453603 | The need to approximate the use-case in clinical machine learning |
Q38752783 | The promise of telemedicine for chronic neurological disorders: the example of Parkinson's disease |
Q104111224 | Unobtrusive detection of Parkinson's disease from multi-modal and in-the-wild sensor data using deep learning techniques |
Q52629010 | Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score. |
Q94474063 | Using an unbiased symbolic movement representation to characterize Parkinson's disease states |
Q33449643 | Using smartphone video "selfies" to monitor change in toothbrushing behavior after a brief intervention: A pilot study |
Q91869411 | Wearable sensors for Parkinson's disease: which data are worth collecting for training symptom detection models |
Q58775378 | Wearables for gait and balance assessment in the neurological ward - study design and first results of a prospective cross-sectional feasibility study with 384 inpatients |
Search more.