Abujrida et al., 2020 - Google Patents
Machine learning-based motor assessment of Parkinson's disease using postural sway, gait and lifestyle features on crowdsourced smartphone dataAbujrida et al., 2020
View PDF- Document ID
- 6002621163911699922
- Author
- Abujrida H
- Agu E
- Pahlavan K
- Publication year
- Publication venue
- Biomedical Physics & Engineering Express
External Links
Snippet
Objectives: Remote assessment of gait in patients' homes has become a valuable tool for monitoring the progression of Parkinson's disease (PD). However, these measurements are often not as accurate or reliable as clinical evaluations because it is challenging to …
- 206010061536 Parkinson's disease 0 title abstract description 153
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1116—Determining posture transitions
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1112—Global tracking of patients, e.g. by using GPS
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Abujrida et al. | Machine learning-based motor assessment of Parkinson’s disease using postural sway, gait and lifestyle features on crowdsourced smartphone data | |
Mahadevan et al. | Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device | |
Stavropoulos et al. | IoT wearable sensors and devices in elderly care: A literature review | |
Chandrabhatla et al. | Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson’s disease motor symptoms | |
Giannakopoulou et al. | Internet of things technologies and machine learning methods for Parkinson’s disease diagnosis, monitoring and management: a systematic review | |
Kim et al. | Wrist sensor-based tremor severity quantification in Parkinson's disease using convolutional neural network | |
Sigcha et al. | Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks | |
Rodríguez-Martín et al. | Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer | |
Aşuroğlu et al. | Parkinson's disease monitoring from gait analysis via foot-worn sensors | |
US20190365332A1 (en) | Determining wellness using activity data | |
Oung et al. | Technologies for assessment of motor disorders in Parkinson’s disease: a review | |
Channa et al. | A-WEAR bracelet for detection of hand tremor and bradykinesia in Parkinson’s patients | |
Juen et al. | Health monitors for chronic disease by gait analysis with mobile phones | |
US20180206775A1 (en) | Measuring medication response using wearables for parkinson's disease | |
Zhan et al. | High frequency remote monitoring of Parkinson's disease via smartphone: Platform overview and medication response detection | |
Phatak et al. | Artificial intelligence based body sensor network framework—narrative review: proposing an end-to-end framework using wearable sensors, real-time location systems and artificial intelligence/machine learning algorithms for data collection, data mining and knowledge discovery in sports and healthcare | |
Kim et al. | The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review | |
Pérez-López et al. | Assessing motor fluctuations in Parkinson’s disease patients based on a single inertial sensor | |
EP3658012A1 (en) | Methods and systems for forecasting seizures | |
Hssayeni et al. | Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III | |
Ma et al. | Quantitative assessment of essential tremor based on machine learning methods using wearable device | |
Hassantabar et al. | Mhdeep: Mental health disorder detection system based on wearable sensors and artificial neural networks | |
Acosta-Escalante et al. | Meta-classifiers in Huntington’s disease patients classification, using iPhone’s movement sensors placed at the ankles | |
Zhao et al. | Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review | |
Molin et al. | Prediction of obstructive sleep apnea using Fast Fourier Transform of overnight breath recordings |