2026-03-06 九州大学
A）退勤後、端末を窓辺などの明るい場所に置き、室内の光から電気をためる。 （B）翌朝、出勤時に名札のように装着し、業務中の行動記録を開始する。 （C・D）デスクワークやオフィス内の移動中も、自動で場所や行動を記録する。 （E）1日の終わりにスマートフォンでデータを確認できる。
＜関連情報＞
- https://www.kyushu-u.ac.jp/ja/researches/view/1431
- https://www.kyushu-u.ac.jp/f/65012/26_0306_01.pdf
- https://www.sciencedirect.com/science/article/pii/S1574119226000210
ZEL+: 持続可能なコンテキストセンシングのための異種エネルギーハーベスターを使用したウェアラブルネットゼロエネルギーライフロギング ZEL+: Wearable net-zero-energy lifelogging using heterogeneous energy harvesters for sustainable context sensing
Mitsuru Arita, Yugo Nakamura, Shigemi Ishida, Yutaka Arakawa
Pervasive and Mobile Computing Available online: 29 January 2026
DOI:https://doi.org/10.1016/j.pmcj.2026.102180
Abstract
This paper presents ZEL+, a wearable lifelogging system designed to operate with net-zero energy consumption by leveraging multiple energy harvesting technologies for continuous context sensing. Self-powered wearable devices often encounter difficulties in environments with inconsistent or low-intensity ambient energy, particularly in indoor settings. To address this challenge, ZEL+ incorporates three key design features. First, it employs a power-switching mechanism based on dual comparators and a capacitor to manage surplus energy and support operation under varying lighting conditions. Second, the system integrates heterogeneous energy harvesters not only as power sources but also as sensing elements. Specifically, a dye-sensitized solar cell provides stable responses under low-light indoor environments, while an amorphous solar cell exhibits sensitivity to changes in ambient illumination; together with a piezoelectric element capturing motion-induced signals, these components contribute complementary cues for location and activity recognition. Third, a Spatial Consistency-Based Correction (SCC) algorithm is applied as a post-processing step to mitigate transient recognition errors and improve the coherence of inferred lifelogs. The system is implemented as a 192 g nametag-shaped wearable device and evaluated in a real-world office environment with 11 participants. Under a person-dependent setting, ZEL+ achieved an accuracy of 96.62% for 8-location place recognition and 97.09% for static/dynamic activity recognition, while maintaining robust performance on more fine-grained tasks. In terms of energy sustainability, the device sustained autonomous operation using harvested energy alone for approximately 93.97% of a standard 8-hour office workday. These results indicate that ZEL+ provides a practical and energy-sustainable solution for continuous lifelogging in indoor mobile computing environments.
