Faculty Profiles - INOUE Sozo

Papers - INOUE Sozo

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Exploring the Use of Accelerometer for Assisting Weight Lifting Exercise Reviewed International journal

Nour Al Bogha, Sozo Inoue, Brahim Brenaissa

International Conference on Activity and Behavior Computing (ABC) 1 - 4 2020.08

Language：English Publishing type：Research paper (international conference proceedings)

Japan Kitakyushu

One of the most important aspects of professional athleticism and rehabilitation is muscle activation. Although sport tracking technologies are advancing
rapidly, existing technologies and devices for detecting muscle performance are still
out of common users' hands, due to their complexity, invasive nature and high cost.
However, sensor equipped devices are getting widely available, which opens the
door to alternative solutions using software. In this work we explore the possibility
of using accelerometer data for muscle performance in a weightlifting exercise. By
investigating a predefined common weightlifting exercise, we exploited a correlation between the acceleration and the lifted weights using a simple RBF prediction
method, in a real experiment where data obtained from three participants. The obtained results show that the weight could be predicted in acceptable accuracy for
each individual, based on acceleration data.

Other Link： https://abc-research.github.io/2020/

Towards New Performance Metrics for Multi-level Data for Activity Recognition Reviewed International journal

Sayeda Shamma Alia, Paula Lago, Sozo Inoue

International Conference on Activity and Behavior Computing (ABC) 1 - 8 2020.08

A Method for Sensor-Based Activity Recognition in Missing Data Scenario Reviewed International journal

Tahera Hossain, Md Atiqur Rahman Ahad, Sozo Inoue

Sensors 20 ( 3811 ) 2 - 3 2020.07

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation Reviewed International journal

Md Atiqur Rahman Ahad, Thanh Trung Ngo, Anindya Das Antar, Masud Ahmed, Tahera Hossain, Daigo Muramatsu, Yasushi Makihara, Sozo Inoue, Yasushi Yagi

Sensors 20 ( 2424 ) 2 - 4 2020.04

2050年の情報処理:7. 近未来を予測できる世界,Ability-awareな世界

Sozo Inoue

61 ( 5 ) 453 - 455 2020.04

医療と介護のIoT

Sozo Inoue

48 ( 1 ) 35 - 41 2020.01

Characterizing Word Embeddings for Zero-Shot Sensor-Based Human Activity Recognition Reviewed International journal

Moe Matsuki, Paula Lago, Sozo Inoue

MDPI Sensors 19 ( 22 ) 1 - 26 2019.11

Language：English Publishing type：Research paper (scientific journal)

In this paper, we address Zero-shot learning for sensor activity recognition using word embeddings. The goal of Zero-shot learning is to estimate an unknown activity class (i.e., an activity that does not exist in a given training dataset) by learning to recognize components of activities expressed in semantic vectors. The existing zero-shot methods use mainly 2 kinds of representation as semantic vectors, attribute vector and embedding word vector. However, few zero-shot activity recognition methods based on embedding vector have been studied; especially for sensor-based activity recognition, no such studies exist, to the best of our knowledge. In this paper, we compare and thoroughly evaluate the Zero-shot method with different semantic vectors: (1) attribute vector, (2) embedding vector, and (3) expanded embedding vector and analyze their correlation to performance. Our results indicate that the performance of the three spaces is similar but the use of word embedding leads to a more efficient method, since this type of semantic vector can be generated automatically. Moreover, our suggested method achieved higher accuracy than attribute-vector methods, in cases when there exist similar information in both the given sensor data and in the semantic vector; the results of this study help select suitable classes and sensor data to build a training dataset.

DOI： 10.3390/s19225043

Kyutacar

Other Link： https://doi.org/10.3390/s19225043

Human Activity Recognition using Earable Device Reviewed

Tahera Hossain, Md. Shafiqul Islam, Md Atiqur Rahman Ahad, Sozo Inoue

ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2019) 5 2019.09

Optimizing Activity Data Collection with Gamification Points Using Uncertainty Based Active Learning Reviewed

Nattaya Mairittha, Tittaya Mairittha, Sozo Inoue

International Workshop on Human Activity Sensing Corpus and Applications 7 pages 2019.09

Nurse Care Activity Recognition Challenge: Summary and Results Reviewed

Paula Lago, Sayeda Shamma Alia, Shingo Takeda, Tittaya Mairittha, Nattaya Mairittha, Farina Faiz, Yusuke Nishimura, Kohei Adachi, Tsuyoshi Okita, Franois Charpillet , Sozo Inoue

Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA) 6 pages 2019.09