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Weapon Activity Recognition
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Proposes an LSTM-based weapon activity recognition system that uses accelerometer data from trigger detection modules during military weapons training to distinguish weapon-related activities.
Problem
Military weapons training relies on observer feedback that cannot scale to many soldiers, and existing systems (MILES) measure accuracy but not correct weapon employment or safety; the paper targets recognition of weapon activities from sensor data.
Approach
Extends soldier activity recognition to weapon activity recognition by collecting accelerometer data from Trigger Detection Modules placed on the weapon grip, rail, and an observer, and training an LSTM (building on the WISDM-extended soldier dataset) to classify weapon activities such as firing, clearing, and reloading.
Key results
- Preliminary confusion matrix for the LSTM is shown in Figure 1; the authors hypothesize clearing an M249 is not confused with other activities, but no numeric accuracy is reported in the text.
Limitations
- Only preliminary results; no reported accuracy numbers or baseline comparisons
- Planned future testing in a military operational environment has not been done
- Clearing activity is only weapon-based activity in the data, so confusion analysis is limited
Experimental setup
Datasets: WISDM extended with soldier activity data (Zhang and Ebling 2023) TDM accelerometer data from M249 and M240 firing, clearing, and reloading
Metrics: accuracy loss confusion matrix
Builds on: WISDM Zhang and Ebling 2023 soldier activity recognition
Closest papers by meaning
- Sensor-Based Activity Recognition via Learning From Distributions (AAAI 2018 · similarity 0.79)
- Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks (CVPR 2025 · similarity 0.78)
- Generalizable Sensor-Based Activity Recognition via Categorical Concept Invariant Learning (AAAI 2025 · similarity 0.76)
- HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors (AAAI 2024 · similarity 0.76)
- TASAR: Transfer-based Attack on Skeletal Action Recognition (ICLR 2025 · similarity 0.76)
- Position: AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research (ICML 2024 · similarity 0.76)
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