Titlebook: IoT Sensor-Based Activity Recognition; Human Activity Recog Md Atiqur Rahman Ahad,Anindya Das Antar,Masud Ahme Book 2021 Springer Nature Sw

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书目名称IoT Sensor-Based Activity Recognition影响因子(影响力)




书目名称IoT Sensor-Based Activity Recognition影响因子(影响力)学科排名




书目名称IoT Sensor-Based Activity Recognition网络公开度




书目名称IoT Sensor-Based Activity Recognition网络公开度学科排名




书目名称IoT Sensor-Based Activity Recognition被引频次




书目名称IoT Sensor-Based Activity Recognition被引频次学科排名




书目名称IoT Sensor-Based Activity Recognition年度引用




书目名称IoT Sensor-Based Activity Recognition年度引用学科排名




书目名称IoT Sensor-Based Activity Recognition读者反馈




书目名称IoT Sensor-Based Activity Recognition读者反馈学科排名

BURSA

Methodology of Activity Recognition: Features and Learning Methods,. The problems of overfitting and underfitting have been discussed with remedies. Previous research works using conventional pattern recognition (PR) approaches on some benchmark datasets have also been analyzed.

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Itinerant

Basic Structure for Human Activity Recognition Systems: Preprocessing and Segmentation,n and criterions to select the best windowing method have been also described based on previous research works. The challenges regarding the selection of window length, window type, choosing overlapping percentage, and the relation between window duration and performance have been also investigated in the end.

似少年

Human Activity Recognition: Data Collection and Design Issues,nd related issues. In this chapter, we present important challenges in activity recognition, data collection protocols, and design issues. This chapter also represents the basic requirement of training data, environmental set up for data collection, sensor requirement, sensor position, and energy consumption issues.

Communicate

Sensor-Based Benchmark Datasets: Comparison and Analysis,ave performed a complete analysis of benchmark datasets, that incorporates information about sensors, attributes, activity classes, etc. These datasets sum up a good number of sensor-based daily activities, medical activities, fitness activities, device usage, fall detection, transportation activity, and hand gesture data.

ENACT

Performance Evaluation in Activity Classification: Factors to Consider,cumulative gains, and lift charts have been explained too. This chapter also represents some essential concepts related to precision and recall trade-off, and accuracy as a performance measure. The contents of this chapter will be useful not only for human activity recognition, but also for other classification-related researches.

Armada

Deep Learning for Sensor-Based Activity Recognition: Recent Trends,ensor-based activity recognition explaining the deep models and their use in previous research works. This chapter also represents the importance of transfer learning and active learning in this field, that are new research topics. Finally, this chapter shows the challenges of using deep models along with feasible solutions.

思考而得

1868-4394 rious aspects of human activity recognition based on wearabl.This book offer clear descriptions of the basic structure for the recognition and classification of human activities using different types of sensor module and smart devices in e.g. healthcare, education, monitoring the elderly, daily huma

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