Khowaja et al., 2016 - Google Patents
AN EFFECTIVE THRESHOLD BASED MEASUREMENT TECHNIQUE FOR FALL DETECTION USING SMART DEVICES.Khowaja et al., 2016
- Document ID
- 15812366200795689298
- Author
- Khowaja S
- Setiawan F
- Prabono A
- Yahya B
- Lee S
- Publication year
- Publication venue
- International Journal of Industrial Engineering
External Links
Snippet
Falls can be considered as most critical events for human workers in real world scenarios which require timely response from the emergency team. Although many have come up with fall detection devices, complex sensors arrangement and response time remain as the …
- 238000001514 detection method 0 title abstract description 115
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
-
- 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
-
- 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
-
- 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
- A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ramachandran et al. | A survey on recent advances in wearable fall detection systems | |
| Lim et al. | Fall‐Detection Algorithm Using 3‐Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model | |
| Igual et al. | Challenges, issues and trends in fall detection systems | |
| Buke et al. | Healthcare algorithms by wearable inertial sensors: a survey | |
| Ntanasis et al. | Investigation of sensor placement for accurate fall detection | |
| Hemmatpour et al. | A review on fall prediction and prevention system for personal devices: evaluation and experimental results | |
| Rescio et al. | Supervised Expert System for Wearable MEMS Accelerometer‐Based Fall Detector | |
| Muheidat et al. | Context-aware, accurate, and real time fall detection system for elderly people | |
| Shawen et al. | Fall detection in individuals with lower limb amputations using mobile phones: machine learning enhances robustness for real-world applications | |
| Purwar et al. | A systematic review on fall detection systems for elderly healthcare | |
| Islam | Automatic fall detection system of unsupervised elderly people using smartphone | |
| Zolfaghari et al. | Sensor-based locomotion data mining for supporting the diagnosis of neurodegenerative disorders: A survey | |
| Nguyen et al. | Falls management framework for supporting an independent lifestyle for older adults: a systematic review | |
| Hussain et al. | Elderly assistance using wearable sensors by detecting fall and recognizing fall patterns | |
| Barrera-Animas et al. | Online personal risk detection based on behavioural and physiological patterns | |
| Liu et al. | Automatic fall risk detection based on imbalanced data | |
| Ramanujam et al. | A vision-based posture monitoring system for the elderly using intelligent fall detection technique | |
| Parmar et al. | A comprehensive survey of various approaches on human fall detection for elderly people | |
| Hemmatpour et al. | Nonlinear Predictive Threshold Model for Real‐Time Abnormal Gait Detection | |
| Birku et al. | Survey on fall detection systems | |
| Khan et al. | Activity detection of elderly people using smartphone accelerometer and machine learning methods | |
| Kambhampati et al. | Unified framework for triaxial accelerometer‐based fall event detection and classification using cumulants and hierarchical decision tree classifier | |
| Lee et al. | Using a smartwatch to detect stereotyped movements in children with developmental disabilities | |
| Khowaja et al. | AN EFFECTIVE THRESHOLD BASED MEASUREMENT TECHNIQUE FOR FALL DETECTION USING SMART DEVICES. | |
| Ahamed et al. | Intelligent fall detection with wearable IoT |