Áp dụng học tăng cường cho bài toán nhận dạng hành động hàng ngày sử dụng dữ liệu từ cảm ứng đơn giản

Áp dụng học tăng cường cho bài toán nhận dạng
hành động hàng ngày sử dụng dữ liệu từ cảm
ứng đơn giản


Tạ Việt Cường


Trường Đại học Công nghệ
Chuyên ngành: Khoa học máy tính; Mã số: 60 48 01
Người hướng dẫn: PGS. TS. Bùi Thế Duy
Năm bảo vệ: 2012


Abstract: Nhận dạng hoạt động hàng ngày trong môi trường thông minh là một
vấn đề đáng quan tâm đối với nhiều ứng dụng trong hệ thống nhà thông minh. Hệ
thống cần phải có khả năng nhận dạng các hành động thay đổi theo thói quen của
người sử dụng. Cho đến nay, đã có một số các mô hình học máy được áp dụng vào
việc nhận dạng hành động. Tuy nhiên, hầu hết đều chỉ hoạt động tốt trong trường
hợp dữ liệu huấn luyện đã được xác định khi học. Trong khi đó, thói quen của
người sự dụng trong môi trường nhà thông minh thay đổi theo thời gian, điều này
yêu cầu các thuật toán học máy phải có khả năng cập nhật lại mô hình khi có dữ
liệu mới.

Keywords: Khoa học máy tính; Bài toán nhận dạng; Khai thác dữ liệu; Dữ liệu từ
cảm ứng

Table of Contents
1 Introduction 2
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Our Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Related Work 5
2.1 Daily Activity Recognition in Smart Home System . . . . . . . . . . 5
2.2 Daily Activity Recognition approach . . . . . . . . . . . . . . . . . . 6
2.3 Incremental learning model . . . . . . . . . . . . . . . . . . . . . . . 7
3 Framework for Activity Recognition in the Home Environment 10
3.1 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Data annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 Feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.4 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4 Growing Neural Gas model 15
4.1 GNG structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.1 Competitive Hebbian Learning . . . . . . . . . . . . . . . . . 17
4.1.2 Weights Adapting . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.1.3 New Node Insertion . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Using GNG network for supervised learning . . . . . . . . . . . . . . 23
4.2.1 Separated Weighted Euclid . . . . . . . . . . . . . . . . . . . . 24
4.2.2 Reduce Overlapping Regions by Using a Local Error Threshold 25
5 Radial Basic Function network 27
5.1 Standard Radial Basic Function . . . . . . . . . . . . . . . . . . . . . 27
5.2 Incremental Radial Basic Function . . . . . . . . . . . . . . . . . . . 29
iv
TABLE OF CONTENTS v
6 Experiment and Result 31
7 Conclusion 35
7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
7.2 Future works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
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