Human Activity and Behavior Analysis relates to the field of vision and sensor-based human action or activity and behavior analysis and recognition. The book includes a series of methodologies, surveys, relevant datasets, challenging applications, ideas, and future prospects.
The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. The book has been divided into two volumes. Volume 1 focuses on relevant activities in three main subject areas: Healthcare and Emotion, Mental Health, and Nurse Care Records. Volume 2 focuses on two main subject areas: Movement and Sensors, and Sports Activity Analysis.
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.
Volume 1
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 OReilly.
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
Volume 2
Preface Movement and Sensors1. Testing the Applicability of Virtual
Stochastic Sensors in Human Activity Recognition Claudia Krull, Pascal
Krenckel, and Lauro Fialho Mu¨ Ller.2. Static Sign Language Recognition Using
Segmented Images and HOG on Cluttered Backgrounds Arezoo Sadeghzadeh, Md
Baharul Islam, and Md Atiqur Rahman Ahad.
3. (k,n)-Threshold Encoding Scheme
for RFID-based Real-Time Event Extraction and Its Application to ADL
Recognition Masayuki Numao and Ryota Fukumoto.
4. A CSI-based Human Activity
Recognition using Canny Edge Detector Hossein Shahverdi, Parisa Fard Moshiri,
Mohammad Nabati, Reza Asvadi, and Seyed Ali Ghorashi.
5. Function Estimation
of Multiple IoT Devices by Communication Traffic Analysis Yuichi Hattori,
Yutaka Arakawa, and Sozo Inoue.
6. A Method for Estimating the Number of
Steps Taken Using a BLE Beacon Attached to the Soles of Footwear Yuki Ogane,
Yu Enokibori, and Katsuhiko Kaji.
7. A Method for Estimating Upper-Arm Muscle
Activities and sEMG with PPG Sensor Masahiro Okamoto and Kazuya Murao.
8.
Development of Automatic Posture and Stumbling Judgement System using Deep
Learning, Jetson Nano and Drone with Information-Sharing Function Shinji
Kawakura, Masayuki Hirafuji, and Ryosuke Shibasaki.
9. Gait condition
assessment methods for visualizing interventional expertise by means of
posture detection Akinori Kunishima, Koki Suzuki, Atsushi Omata, Shogo
Ishikawa, and Shinya Kiriyama.
10. Psychological Analysis in Human-Robot
Collaboration from Workplace Stress Factors: A Review Nazmun Nahid, Min
Xinyi, Md Atiqur Rahman Ahad, and Sozo Inoue. Sports Activity Analysis.
11.
Real-Time Feedback System for Efficient Core Training Keisuke Sato, Ami
Jinno, Nishiki Motokawa, Tahera Hossain, Anna Yokokubo, and Guillaume
Lopez.12. Keeping athletes motivated by realtime co-running application Shun
Ishii, Kazuki Imura, Tahera Hossain, Anna Yokokubo, and Guillaume Lopez.
13.
Boxing movements recognition using IMUs during shadow boxing exercise
Yoshinori Hanada, Tahera Hossain, Anna Yokokubo, and Guillaume Lopez.
14.
FootbSense: Soccer Moves in Practice Environment Identification Using a
Single IMU Hikari Aoyagi, Tahera Hossain, Anna Yokokubo, and Guillaume Lopez.
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.
