Título:
Human activity recognition using machine learning techniques in a low-resource embedded system
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Autor:
Stolovas, Ilana
Suárez, Santiago
Pereyra, Diego
De Izaguirre, Francisco
Cabrera, Varinia
Suárez, Santiago
Pereyra, Diego
De Izaguirre, Francisco
Cabrera, Varinia
Tutor:
Tipo de documento:
Preprint
Editor:
Palabras clave:
Human Activity Recognition
Acceleration Sensor
Linear Discriminant Analysis
Support Vector Machines
Acceleration Sensor
Linear Discriminant Analysis
Support Vector Machines
Descriptores:
Año de publicación:
2021
Contenido:
Resumen:
Human activity recognition aims to infer a person’s actions from a set of observations captured by several sensors. Data acquisition, processing and inference on edge devices add a complexity factor to the task, as they involve a trade-off between hardware efficiency and performance. We present a prototype of a wearable device that identifies a person’s activity: walking, running or staying still. The system consists of a Texas Instruments MSP-EXP430G2ET launchpad, connected to a BOOSTXL-SENSORS boosterpack with a BMI160 accelerometer. The designed prototype can take acceleration measurements, process them and either transmit them to a computer or classify the activity in the microcontroller. Additionally, our system has LEDs to display coloured signals according to the inferred activity in real-time. The classification algorithm is based on the calculation of statistical features (mean, standard deviation, maximum and minimum) for each accelerometer axis, the application of a dimensionality reduction algorithm (LDA, Linear Discriminant Analysis) and an SVM (Support Vector Machines) classification model.
Descripción:
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Editorial:
Udelar.FI.
EN:
IEEE URUCON 2021, Montevideo, Uruguay, 24-26 nov 2021, pp. 1-5.
Financiadores:
Este trabajo fue parcialmente financiado por la Comisión Académica de Posgrado (CAP, UdelaR), Espacio Interdisciplinario (EI, UdelaR) y la Comisión Sectorial de Investigación Científica (CSIC, UdelaR) “Proyecto I + D : Sistema electrónico para la caracterización del comportamiento de ovinos".
Citación:
Stolovas, I., Suárez, S., Pereyra, D. y otros. Human activity recognition using machine learning techniques in a low-resource embedded system [Preprint]. Publicado en : IEEE URUCON 2021, Montevideo, Uruguay, 24-26 nov 2021, 5 p.
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Departamento académico:
Electrónica
Grupo de investigación:
Microelectrónica
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| SSPDC21.pdf | Preprint | 579.27 KB | Adobe PDF |