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A simple design of wearable device for fall detection with accelerometer and gyroscope
Nari M.I.a, Suprapto S.S.a, Kusumah I.H.a, Adiprawita W.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2016 IEEE.Falling is one of the most common injurious accidents in the elderly which could result in pain, disability, and even death. About 33% of the elderly have falling accident each year. According to the Statistics Indonesia data in 2014, the total population of over-60-year-olds increase to 8.03% of the total population in that year. In this research, we use the accelerometer and a gyroscope sensor for fall detection. The device is placed in the position of the waist. This study uses 6 type activity comparators such as walking, running, upstairs, down stairs, sit down quickly and stand up quickly. These tests are also used for 3 falling accidents such as falling forward, to the right and to the left. By comparing the results of these tests, the falling threshold values can be acquired. The threshold values of A, B and C are 0.6, 1.7 and 175 respectively. Results show that the system can detect sensitivity and specificity up to 90% and 86.7% respectively.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Elderly,Fall detection,Gyroscope sensors,Indonesia,Sensitivity and specificity,Wearable devices,Wireless sensor system[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Accelerometer,Elderly,Fall Detection,Gyroscope,Wireless Sensor System[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ISESD.2016.7886698[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]