Human Activity and Behavior Analysis
CRC Press (Verlag)
978-1-032-44311-9 (ISBN)
The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. This volume focuses on relevant activities in three main subject areas: Healthcare and Emotion, Mental Health, and Nurse Care Records.
The editors are experts in these arenas and the contributing authors are drawn from high-impact research groups around the world. This book will be of great interest to academics, students, and professionals working and researching in the field of human activity and behavior analysis.
Md Atiqur Rahman Ahad, PhD, is Associate Professor at the University of East London, UK. Sozo Inoue, PhD, is Professor at the Kyushu Institute of Technology, Japan. Guillaume Lopez, PhD, is Professor at Aoyama Gakuin University, Japan. Tahera Hossain, PhD, is Assistant Professor (Project) at Aoyama Gakuin University, Japan.
Preface. Healthcare and Emotion. 1. Forecasting Parkinson's Disease Patients' Wearing-Off using Wrist-Worn Fitness Tracker and Smartphone Dataset John Noel Victorino, Yuko Shibata, Inoue Sozo, and Tomohiro Shibata. 2. Toward Human Thermal Comfort Sensing: New Dataset and Analysis of Heart Rate Variability (HRV) Under Different Activities Tahera Hossain, Yusuke Kawasaki, Kazuki Honda, Kizito Nkurikiyeyezu, and Guillaume Lopez. 3. Reducing the Number of Wearable Sensors and Placement Optimization by Missing Data Imputation on Nursery Teacher Activity Recognition Akira Omi, Kensi Fujiwara, Naoko Ishibashi, and Ren Ohmura. 4. Optimal EEG Electrode Set for Emotion Recognition from Brain Signals: An Empirical Quest Rumman Ahmed Prodhan, Sumya Akter, Tanmoy Sarkar Pias, and Md. Akhtaruzzaman Adnan. 5. Translation-Delay-Aware Emotional Avatar System for Online Communication Support Tomoya Suzuki, Akihito Taya, Yoshito Tobe, and Guillaume Lopez. 6. Touching with eye contact and vocal greetings increases the sense of security Miyuki Iwamoto and Atsushi Nakazawa. 7. Challenges and Opportunities of Activity Recognition in Clinical Pathways Christina Garcia and Sozo Inoue. Mental Health. Anxolotl, an Anxiety Companion App - Stress Detection Nuno Gomes, Matilde Pato, Pedro Santos, Andre´ Lourenc¸ and Lourenc Rodrigues. 9. Detection of self-reported stress level from wearable sensor data using machine learning and deep learning-based classifiers: Is it feasible? Atzeni Michele, Cossu Luca, Cappon Giacomo, and Vettoretti Martina. 10. A Multi-Sensor Fusion Method for Stress Recognition Leonardo Alchieri, Nouran Abdalazim, Lidia Alecci, Silvia Santini, and Shkurta Gashi. 11. Classification of Stress via Ambulatory ECG and GSR Data Zachary Dair, Muhammad Saad, Urja Pawar, Samantha Dockray, and Ruairi O’Reilly. 12. Detection and Classification of Acute Psychological Stress in Free-Living: Challenges and Achievements M. Sevil, M. Rashid, R. Askari, L. Sharp, L. Quinn, and A. Cinar 13. IEEE EMBC 2022 Workshop and Challenge on Detection of Stress and Mental Health Using Wearable Sensors Huiyuan Yang, Han Yu, Alicia Choto Segovia, Maryam Khalid, Thomas Vaessen, and Akane Sano. 14. Understanding Mental Health Using Ubiquitous Sensors and Machine Learning: Challenges Ahead Tahia Tazin, Tahera Hossain, Shahera Hossain, and Sozo Inoue. Nurse Care Records. 15. Improving Complex Nurse Care Activity Recognition Using Barometric Pressure Sensors Muhammad Fikry, Christina Garcia, Vu Nguyen Phuong Quynh, Shin- taro Oyama, Keiko Yamashita, Yuji Sakamoto, Yoshinori Ideno, and Sozo Inoue. 16. Analysis of Care Records for Predicting Urination Times Masato Uchimura, Haru Kaneko, and Sozo Inoue. 17. Predicting User-specific Future Activities using LSTM-based Multi-label Classification Mohammad Sabik Irbaz, Fardin Ahsan Sakib, and Lutfun Nahar Lota. 18. Nurse Activity Recognition based on Temporal Frequency Features Md. Sohanur Rahman, Hasib Ryan Rahman, Abrar Zarif, Yeasin Arafat Pritom, and Md Atiqur Rahman Ahad. 19. Ensemble Classifier for Nurse Care Activity Prediction Based on Care Records Bj¨orn Friedrich andAndreas Hein. 20. Addressing the inconsistent and missing time stamps in Nurse Care Activity Recognition Care Record Dataset Rashid Kamal, Chris Nugent, Ian Cleland, and Paul McCullagh. 21. A Sequential-based Analytical Approach for Nurse Care Activity Forecasting Md Mamun Sheikh, Shahera Hossain, and Md Atiqur Rahman Ahad. 22. Predicting Nursing Care with K-Nearest Neighbors and Random Forest Algorithms Jonathan Sturdivant, John Hendricks, and Gulustan Dogan. 23. Future Prediction for Nurse Care Activities Using Deep Learning based Multi-Label Classification Md. Golam Rasul, Wasim Akram, Sayeda Fatema Tuj Zohura, Tanjila Alam Sathi, and Lutfun Nahar Lota. 24. A Classification Technique based on Exploratory Data Analysis for Activity Recognition Riku Shinohara, Huakun Liu, Monica Perusqu´Ia-Hern´Andez, Naoya Isoyama, Hideaki Uchiyama, and Kiyoshi Kiyokawa/ 25. Time Series Analysis of Care Records Data for Nurse Activity Recognition in the Wild Md. Kabiruzzaman, Mohammad Shidujaman, Shadril Hassan Shifat, Pritom Debnath, and Shahera Hossain. 26. Summary of the Fourth Nurse Care Activity Recognition Challenge – Predicting Future Activities. 27. Defry Hamdhana, Christina Garcia, Nazmun Nahid, Haru Kaneko, Sayeda Shamma Alia, Tahera Hossain, and Sozo Inoue
| Erscheinungsdatum | 19.04.2024 |
|---|---|
| Reihe/Serie | Ubiquitous Computing, Healthcare and Well-being |
| Zusatzinfo | 96 Tables, black and white; 150 Halftones, black and white; 150 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 1000 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Recht / Steuern ► Privatrecht / Bürgerliches Recht ► IT-Recht | |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 1-032-44311-1 / 1032443111 |
| ISBN-13 | 978-1-032-44311-9 / 9781032443119 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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