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Citation |
Bharathkumar K, Paolini C, Sarkar M. Proc. IEEE Global Humanit. Technol. Conf. 2020; 2020: e9342948. |
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Copyright |
(Copyright © 2020, IEEE) |
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DOI |
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PMID |
36760805 |
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PMCID |
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Abstract |
In the geriatric population, physical injuries sustained by an unintentional or an unpredictable fall on a hard surface is the leading cause of injury related morbidity and sometimes mortality. Each year, close to 30% of adults around the age group of 65 fall down at least once. In the year 2015, close to 2.9 million falls were reported, resulting in 33,000 deaths. As much as 61% of elderly nursing home residents fell at some point during their first year of residence.These falls may aggravate the situation leading to bone fracture, concussion, internal bleeding or traumatic brain injury when immediate medical attention is not offered to the person. Delay in course of the event may sometimes lead to death as well. Recently, many studies have come up with wearable devices. These devices that are now commercially available in the market are small, compact, wireless, battery operated and power efficient. This study discusses the findings that the optimal location for a Fall Detection Sensor on the human body is in front of the Shin bone. This is based on the 183 features collected from Inertial Measurement Unit (IMU) sensors placed on 16 human body locations and trained-tested using Convolutional Neural Networks (CNN) machine learning paradigm. The ultimate goal is to develop a mobile, wireless, wearable, low-power medical device that uses a small Lattice iCE40 Field Programmable Gate Array (FPGA) integrated with gyro and accelerometer sensors which detects whether the device wearer has fallen or not. This FPGA is capable of realizing the Neural Network model implemented in it. This Insitu or Edge inferencing wearable device is capable of providing real-time classifications without any Transmitting or Receiving capabilities over a wireless communication channel. Language: en |
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Keywords |
machine learning; edge inferencing; fall detection sensor; FDS; FPGA; neural networks |