PMID- 36760805
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 2377-6919 (Print)
IS  - 2473-5728 (Electronic)
IS  - 2377-6919 (Linking)
VI  - 2020
DP  - 2020 Oct-Nov
TI  - FPGA-based Edge Inferencing for Fall Detection.
LID - 10.1109/ghtc46280.2020.9342948 [doi]
AB  - 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.
FAU - Bharathkumar, Kishore
AU  - Bharathkumar K
AD  - Electrical and Computer Engineering, San Diego State University, San Diego, USA.
FAU - Paolini, Christopher
AU  - Paolini C
AD  - Electrical and Computer Engineering, San Diego State University, San Diego, USA.
FAU - Sarkar, Mahasweta
AU  - Sarkar M
AD  - Electrical and Computer Engineering, San Diego State University, San Diego, USA.
LA  - eng
GR  - U54 MD012397/MD/NIMHD NIH HHS/United States
PT  - Journal Article
PL  - United States
TA  - Proc IEEE Glob Humanit Technol Conf
JT  - Proceedings. IEEE Global Humanitarian Technology Conference
JID - 101712954
PMC - PMC9908270
MID - NIHMS1867426
OTO - NOTNLM
OT  - FDS
OT  - FPGA
OT  - edge inferencing
OT  - fall detection sensor
OT  - machine learning
OT  - neural networks
EDAT- 2020/10/01 00:00
MHDA- 2020/10/01 00:01
PMCR- 2023/02/08
CRDT- 2023/02/10 03:01
PHST- 2023/02/10 03:01 [entrez]
PHST- 2020/10/01 00:00 [pubmed]
PHST- 2020/10/01 00:01 [medline]
PHST- 2023/02/08 00:00 [pmc-release]
AID - 10.1109/ghtc46280.2020.9342948 [doi]
PST - ppublish
SO  - Proc IEEE Glob Humanit Technol Conf. 2020 
      Oct-Nov;2020:10.1109/ghtc46280.2020.9342948. doi: 10.1109/ghtc46280.2020.9342948.