Detecting Accuracy of Accelerometer and Gyroscope Wearable Shimmer Sensors using Linear Regression
Jana Shafi1, P. Venkata Krishna2
1Jana Shafi, Research Scholar, Department of Computer and Engineering Science, SPMVV University Tirupati, A.P., India.
2P. Venkata Krishna, Department of Computer Science and Engineering, SPMVV University, Tirupati, A.P, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1992-1998| Volume-8 Issue-5, January 2020. | Retrieval Number: E6025018520/2020©BEIESP | DOI: 10.35940/ijrte.E6025.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Smart Wearable can measures physical activities by analyzing the user’s body movements which requires existing sensors such as accelerometer and gyroscope in order to secure perilous data so that it should not go missing at the point of high speed rotation or high impact. Accelerometer is a sensor that has been generally recognized as suitable and practical in smart wearable devices to measure and assess human physical activities. Wearable accelerometer sensor affords easily moveable systems that stream real-time data. The gyroscope sensor track human movement and activity and improves its accuracy. In this paper, we are comparative analyzing the commercially available SHIMMER3 wearable 3rd generation sensors raw data of age under 71-80+ people from two different sensors i.e. accelerometer and gyroscope and modeling it with machine learning approach of linear regression.
Keywords: Sensors, Wearable, Accelerometer, Gyroscope.
Scope of the Article: Personal and Wearable Networks.