Nothing Special   »   [go: up one dir, main page]

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 data

Abujrida 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 …
Continue reading at web.cs.wpi.edu (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-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
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116Determining posture transitions
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1112Global tracking of patients, e.g. by using GPS
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10Services
    • G06Q50/22Health care, e.g. hospitals; Social work
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7232Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801Arrangements 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
    • A61B5/6802Sensor mounted on worn items
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/0002Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/40Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076Diagnosing 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