.png){kind=icon}
返回
Maximum Entropy Markov Model for Human Activity Recognition Using Depth Camera
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3132559.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Activity recognition is an essential factor in the determination of daily routine of a human being. There exist numerous Human Activity Recognition (HAR) systems; however, an HAR with a practical accuracy is still in search and a challenge at large. The classifiers utilized in existing systems offer degrading recognition rates in various key environments such as depth camera environment among others. To address the limitation of degrading recognition rates, in this paper, we propose Maximum Entropy Markov Model (MEMM) that solves the degrading recognition rate problem. In MEMM, we model the states of activity recognition as the states of the model itself i.e. as the states of MEMM, and hence consider the observations of video-sensor as the observations of MEMM. Further, we use a modified version of Viterbi, a machine learning algorithm, to generate the most likely probable state sequence based on these observations. Then, from such a state sequence, we use MEMM to predict the activity state. We evaluate the performance MEMM against depth dataset having eleven different types of activities in a large-scale experimentation process. The results show that MEMM outperforms existing well-known methods by achieving a weighted average recognition rate 96.3% across the naturalistic dataset collected using depth camera.
Keyword:
Feature extraction
Hidden Markov models
Activity recognition
Cameras
Mathematical models
Wavelet transforms
Telemedicine
Healthcare
machine learning
activity recognition
depth camera
MEMM
state-of-the-art
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏The Demand for International Reserves and Exchange Rate Adjustments: The Case of LDCs, 1964-1972.png)
Economica
OAAI
EconomicaIF0
OAAI分享收藏A Real-Time Human Action Recognition System Using Depth and Inertial Sensor Fusion基于深度和惯性传感器融合的实时人体动作识别系统IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏Crystalline‐State Reaction with Allosteric Effect in Spin‐Crossover, Interpenetrated Networks with Magnetic and Optical Bistability具有磁和光学双稳态的自旋交叉，互穿网络中具有变构效应的晶态反应
PREAI
分享收藏
查看更多内容