Cahoolessur et al., 2020 - Google Patents
Fall detection system using XGBoost and IoTCahoolessur et al., 2020
View HTML- Document ID
- 1463287009259397955
- Author
- Cahoolessur D
- Rajkumarsingh B
- Publication year
- Publication venue
- R&D Journal
External Links
Snippet
This project aims to design and implement a fall detection system for the elders using machine learning techniques and Internet-of-Things (IoT). The main issue with fall detection systems is false alarms and hence incorporating machine learning in the fall detection …
- 238000001514 detection method 0 title abstract description 98
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
- 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
- G08B21/0202—Child monitoring systems using a transmitter-receiver system carried by the parent and the child
-
- 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
-
- 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
- G08B21/12—Alarms for ensuring the safety of persons responsive to undesired emission of substances, e.g. pollution alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/016—Personal emergency signalling and security systems
-
- 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
- G08B21/18—Status alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/009—Signalling of the alarm condition to a substation whose identity is signalled to a central station, e.g. relaying alarm signals in order to extend communication range
-
- 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
- A61B5/103—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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ajerla et al. | A real‐time patient monitoring framework for fall detection | |
Mrozek et al. | Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge | |
Casilari et al. | Analysis of android device-based solutions for fall detection | |
Cahoolessur et al. | Fall detection system using XGBoost and IoT | |
Musci et al. | Online fall detection using recurrent neural networks on smart wearable devices | |
Luque et al. | Comparison and characterization of android-based fall detection systems | |
Del Rosario et al. | Tracking the evolution of smartphone sensing for monitoring human movement | |
Kwon et al. | Recognition of daily human activity using an artificial neural network and smartwatch | |
Erdogan et al. | A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data | |
Shawen et al. | Fall detection in individuals with lower limb amputations using mobile phones: machine learning enhances robustness for real-world applications | |
AU2017251830A1 (en) | Activity recognition using accelerometer data | |
Qi et al. | A survey of physical activity monitoring and assessment using internet of things technology | |
Elbasiony et al. | A survey on human activity recognition based on temporal signals of portable inertial sensors | |
Guvensan et al. | An energy-efficient multi-tier architecture for fall detection on smartphones | |
Soni et al. | An approach to enhance fall detection using machine learning classifier | |
Zhang et al. | A novel fuzzy logic algorithm for accurate fall detection of smart wristband | |
Parmar et al. | A comprehensive survey of various approaches on human fall detection for elderly people | |
Gharghan et al. | A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques | |
Fanez et al. | Mixing user-centered and generalized models for fall detection | |
Santoyo-Ramón et al. | A study on the impact of the users’ characteristics on the performance of wearable fall detection systems | |
Dinh et al. | A fall and near-fall assessment and evaluation system | |
Matos-Carvalho et al. | Sensitivity analysis of LSTM networks for fall detection wearable sensors | |
Villar et al. | Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges | |
Chakraborty et al. | A deep-CNN based low-cost, multi-modal sensing system for efficient walking activity identification | |
González-Cañete et al. | Consumption analysis of smartphone based fall detection systems with multiple external wireless sensors |