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Asking a Multimodal LLM Whether Someone Fell: Zero-Shot Screening of Low-Resolution Thermal Video in a Laboratory and an Occupied Home

Marcos Junca, Mauro Junca · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29913 · cited by 0 works (OpenAlex) · open-access PDF

Korean

두 개의 센서 카메라가 뜨거운 비행기 릴리스하지만 일반적인 픽셀은 얼굴을 캡처하지 않고 혼자 살고있는 사람들에 대한 경보 데이터를 감시 할 수 있으며, 멀티 모드 큰 언어 모델 (LLMs)는 작업 특정 훈련없이 집에서 짧은 비디오를 판단 할 수 있습니다. 우리는 생산의 멀티 모드 LLM, Gemini 3.8 플래시를 0. 온도에서 살고있다. 우리는 사건을 유발하지 않은 낙하 스크리너의 결정 단계로, 공개 32x24 열 데이터 세트에서 정확하게 사용 : 짧은 열 클립 당 하나의 요청을 사용합니다. 낙하 된 주제의 69 % (모든 주제 위에서 67 %의 소리 트랙이 떨어지며 볼 수있는 침대는 그것을 제외

Machine translation (Meta M2M100); the English original follows.

Abstract

Thermal cameras can watch over older people living alone without capturing faces, and multimodal large language models (LLMs) can judge a short video without task-specific training. We evaluate a production multimodal LLM, Gemini 3.8 Flash at temperature 0, as the decision stage of an event-triggered fall screener, used exactly as a deployed prototype uses it: one request per short thermal clip, with its sound track when there is one, and its predecessor Gemini 3.7 Flash on the same requests. On 438 staged falls and 563 non-fall windows that we located by hand in a public 32x24 thermal dataset, the deployed request called 69% of the held-out subjects' falls (67% over all subjects; bed falls excluded) and none of 499 non-fall windows, including people lying on the floor and getting up. Over 76 h of ordinary life in an occupied room, recorded at 160x120 with sound, it called none of 395 clips a fall (95% upper bound 1.2 false alarms per day) and caught 4 of the 9 staged falls the camera could see. One cautionary sentence in the prompt ("a person who goes out of view ... is not a fall") accounted for many of the misses: removing it raised sensitivity on the held-out subjects from 69% to 83% on 3.8 Flash and from 80% to 92% on Gemini 3.7 Flash, and in the home from 4 to 9 of the 9 visible falls, with no false call in ordinary living but at the price of calling deliberate lie-downs a fall. Sensitivity collapsed at 8x6 pixels (34%); identical requests disagreed on 4% of windows; and when the model called a fall, its explanation placed it within a second of the annotated landing 99% of the time. Two conventional detectors trained on the laboratory data caught nearly every held-out laboratory fall but raised 14.2 and 23.9 false alarms per day in the home. We release the frame-level fall annotations of the public dataset and every model answer on it.

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Topics: CLIPS, Flash (photography), Computer science, Sentence, Artificial intelligence, Sensitivity (control systems)

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