Personalized Activity Recognition with Deep Triplet Embeddings

Personalized Activity Recognition with Deep Triplet Embeddings

A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized activity recognition based on deep feature repres...

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Bibliographic Details
Main Authors: David Burns, Philip Boyer, Colin Arrowsmith, Cari Whyne
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Sensors
Subjects:
human activity recognition
personalized algorithms
machine learning
time series
triplet neural network
inertial sensors
Online Access:https://www.mdpi.com/1424-8220/22/14/5222

Internet

https://www.mdpi.com/1424-8220/22/14/5222

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