[go: up one dir, main page]

Khawandi et al., 2013 - Google Patents

Applying machine learning algorithm in fall detection monitoring system

Khawandi et al., 2013

Document ID
8636463657219767999
Author
Khawandi S
Ballit A
Daya B
Publication year
Publication venue
2013 5th International Conference and Computational Intelligence and Communication Networks

External Links

Snippet

Fall is a major health hazard for the elders when they live independently. Approximately a third of those aged 65 years and over fall each year. An automatic fall detector ensures the best possible chance of a full recovery following a fall. This paper presents new algorithm …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal operating condition and not elsewhere provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0438—Sensor means for detecting
    • 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
    • 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
    • A61B5/1117—Fall detection
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal operating condition and not elsewhere provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
    • 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
    • A61B5/1123—Discriminating type of movement, e.g. walking or running
    • 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
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
    • 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
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402—Electrocardiography, i.e. ECG

Similar Documents

Publication Publication Date Title
Xefteris et al. Performance, challenges, and limitations in multimodal fall detection systems: A review
Chelli et al. A machine learning approach for fall detection and daily living activity recognition
Erden et al. Sensors in assisted living: A survey of signal and image processing methods
Mubashir et al. A survey on fall detection: Principles and approaches
Koshmak et al. Challenges and issues in multisensor fusion approach for fall detection
Doukas et al. Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components
Lawal et al. Deep human activity recognition using wearable sensors
Fan et al. Robust unobtrusive fall detection using infrared array sensors
Hossain et al. A direction-sensitive fall detection system using single 3D accelerometer and learning classifier
Merrouche et al. Depth camera based fall detection using human shape and movement
Hemalatha et al. Frequent bit pattern mining over tri-axial accelerometer data streams for recognizing human activities and detecting fall
Shi et al. Fall Detection Algorithm Based on Triaxial Accelerometer and Magnetometer.
Nizam et al. A study on human fall detection systems: Daily activity classification and sensing techniques
Spasova et al. A survey on automatic fall detection in the context of ambient assisted living systems
Ramachandran et al. Machine learning-based techniques for fall detection in geriatric healthcare systems
Parmar et al. A comprehensive survey of various approaches on human fall detection for elderly people
Khawandi et al. Applying machine learning algorithm in fall detection monitoring system
Birku et al. Survey on fall detection systems
Kerdegari et al. Development of wearable human fall detection system using multilayer perceptron neural network
Shahiduzzaman Fall detection by accelerometer and heart rate variability measurement
Shaima Fall detection and monitoring using machine learning: a comparative study
Hassan et al. Comparative analysis of machine learning algorithms for classification of environmental sounds and fall detection
Boudouane et al. Portable fall detection system using a triaxial accelerometer based on a thresholds algorithm
Nguyen et al. The internet-of-things based fall detection using fusion feature
Nouriani et al. System-identification-based activity recognition algorithms with inertial sensors