Abstract
Wearable accelerometers are used for a wide range of applications, such as gesture recognition, gait analysis, and sports monitoring. Yet most existing foundation models focus primarily on classifying common daily activities such as locomotion and exercise, limiting their applicability to the broader range of tasks that rely on other signal characteristics. We present SlotFM, an accelerometer foundation model that generalizes across diverse downstream tasks. SlotFM uses Time-Frequency Slot Attention, an extension of Slot Attention that processes both time and frequency representations of the raw signals. It generates multiple small embeddings (slots), each capturing different signal components, enabling task-specific heads to focus on the most relevant parts of the data. We also introduce two loss regularizers that capture local structure and frequency patterns, which improve reconstruction of fine-grained details and helps the embeddings preserve task-relevant information. We evaluate SlotFM on 16 classification and regression downstream tasks that extend beyond standard human activity recognition. It outperforms existing self-supervised approaches on 13 of these tasks and achieves comparable results to the best performing approaches on the remaining tasks. On average, our method yields a 4.5% performance gain, demonstrating strong generalization for sensing foundation models.
Abstract (translated)
可穿戴加速度计被广泛应用于多种应用场景，例如手势识别、步态分析和运动监测。然而，现有的大多数基础模型主要关注于分类日常活动（如行走和锻炼），这限制了它们在依赖其他信号特征的更广泛的任务中的适用性。我们提出了SlotFM，这是一种适用于各种下游任务的加速度计基础模型。 SlotFM采用了时频槽注意力机制（Time-Frequency Slot Attention），它是槽注意力机制的一种扩展形式，可以处理原始信号的时间和频率表示。它生成多个小型嵌入（slot），每个嵌入捕捉不同的信号组成部分，从而使特定于任务的头部能够专注于数据中最相关的部分。我们还引入了两个损失正则化器来捕获局部结构和频域模式，这有助于重建细粒度细节并帮助嵌入保留与任务相关的信息。 我们在16个分类和回归下游任务上评估了SlotFM，这些任务超出了标准的人体活动识别范围。在其中的13项任务中，SlotFM的表现优于现有的自监督方法，并且在其余的任务中取得了接近最佳的方法的成绩。总体而言，我们的方法平均提高了4.5%的性能，这表明对于感知基础模型来说，具有强大的泛化能力。
URL
https://arxiv.org/abs/2509.21673
