By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting
H.S. Yoon, Biniyam Aschalew Tolera, Taesik Gong, Kimin Lee, Sung-Ju Lee · 2024
Large language models (LLMs) have demonstrated exceptional abilities across various domains.However, utilizing LLMs for ubiquitous sensing applications remains challenging as existing text-prompt methods show significant performance degradation when handling long sensor data sequences.We propose a visual prompting approach for sensor data using multimodal LLMs (MLLMs).We design a visual prompt that directs MLLMs to utilize visualized sensor data alongside the target sensory task descriptions.Additionally, we introduce a visualization generator that automates the creation of optimal visualizations tailored to a given sensory task, eliminating the need for prior task-specific knowledge.We evaluated our approach on nine sensory tasks involving four sensing modalities, achieving an average of 10% higher accuracy than text-based prompts and reducing token costs by 15.8×.Our findings highlight the effectiveness and cost-efficiency of visual prompts with MLLMs for various sensory tasks.The source code is available at https://github. com/diamond264/ByMyEyes. ### InstructionYou are an expert in sensor data analysis.Given the sensor data, determine the correct answer from the options listed in the question.Provide the answer with the format of ANSWER , where ANSWER corresponds to one of the options listed in the question.If the answer is not in the options, choose the most possible option.The ECG data is collected from a lead II ECG sensor.The ECG data is recorded over 10 seconds.The data is normalized with the statistics of the user's data.Please refer to the provided examples and use them to answer the following question for the target data.### Examples *Example of normal*: Average heartbeat in the ECG signal (list of ['lead II']): [-0.34, -0.34, -0.35, -0.35, -0.36, … ECG_P_Peaks in the ECG signal (list of (index, value)): [(21, 0.08), (129, 0.19), (239, 0.22), … ECG_Q_Peaks in the ECG signal (list of (index, value)): [(35, -0.61), (137, -0.46), (246, -0.48), … ECG_S_Peaks in the ECG signal (list of (index, value)): [(42, -1.4), (149, -1.35), (260, -1.33), … ECG_T_Peaks in the ECG signal (list of (index, value)): [(63, 2.18), (171, 2.11), (282, 2.31), … *Example of conduction disturbance*: Average heartbeat in the ECG signal (list of ['lead II']): [-0.15, -0.24, -0.29, -0.31, -0.28, … ECG_P_Peaks in the ECG signal (list of (index, value)): [(4, 0.14), (57, 0.3), (103, 0.22), … ECG_Q_Peaks in the ECG signal (list of (index, value)): [(14, -0.05), (65, -0.05), (109, -0.07), … ECG_S_Peaks in the ECG signal (list of (index, value)): [(22, -2.28), (73, -2.1), (124, -2.35), … ECG_T_Peaks in the ECG signal (list of (index, value)): [(82, 0.03), (142, 0.64), (245, 0.25), … ### Question Average heartbeat in the ECG signal (list of ['lead II']): [-0.39, -0.39, -0.39, -0.39, -0.4,… ECG_P_Peaks in the ECG signal (list of (index, value)): [(15, 0.14), (94, -0.26), (173, -0.23), … ECG_Q_Peaks in the ECG signal (list of (index, value)): [(23, -0.27), (102, -0.81), (182, -0.55), … ECG_S_Peaks in the ECG signal (list of (index, value)): [(34, 0.15), (116, -0.51), (192, -0.45), … ECG_T_Peaks in the ECG signal (list of (index, value)): [(50, 1.39), (130, 1.1), (209, 1.31), … *Question*: When the sensor data is used for a task for classifying ECG data into 2 categories: conduction disturbance, normal, what is the most likely answer among ['conduction disturbance', 'normal']?*Answer*: