iMove：探索用于健身活动识别的生物阻抗感知
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition
AI总结：
提出iMove框架，通过传感器融合与对比学习利用生物阻抗增强IMU的健身活动识别，在推理阶段仅需IMU即可提升分类准确率。
摘要：
自动且精确的健身活动识别在促进健康生活方式和个性化预防性医疗保健等方面大有裨益。尽管 IMU 是目前主要的健身追踪模态，但通过 iMove，我们展示了生物阻抗能够通过传感器融合和对比学习来帮助改进基于 IMU 的健身追踪。为了评估我们的方法，我们进行了一项实验，包含 10 名受试者在 5 天内执行的 6 项上半身健身活动，以收集来自两个手腕的生物阻抗和左腕 IMU 的同步数据。该对比学习框架利用这两种模态来训练一个更好的纯 IMU 分类模型，其中生物阻抗仅在训练阶段需要。通过这种方式，在输入单个 IMU 的情况下，平均 Macro F1 分数提高了 3.22%，达到 84.71%，而 IMU 基线模型为 81.49%。我们还展示了生物阻抗如何通过传感器融合直接改进人体活动识别（HAR），达到了 89.57% 的平均 Macro F1 分数（训练和推理均需要两种模态），即使单独使用生物阻抗的平均 Macro F1 分数仅为 75.36%，逊于单独使用 IMU。此外，在一项关于下半身健身活动分类的扩展研究中也获得了类似结果，证明了我们方法的泛化能力。我们的发现强调了传感器融合和对比学习作为推进健身活动识别的宝贵工具的潜力，其中生物阻抗在增强基于 IMU 系统的能力方面发挥着关键作用。
英文摘要：
Automatic and precise fitness activity recognition can be beneficial in aspects from promoting a healthy lifestyle to personalized preventative healthcare. While IMUs are currently the prominent fitness tracking modality, through iMove, we show bio-impedence can help improve IMU-based fitness tracking through sensor fusion and contrastive learning.To evaluate our methods, we conducted an experiment including six upper body fitness activities performed by ten subjects over five days to collect synchronized data from bio-impedance across two wrists and IMU on the left wrist.The contrastive learning framework uses the two modalities to train a better IMU-only classification model, where bio-impedance is only required at the training phase, by which the average Macro F1 score with the input of a single IMU was improved by 3.22 \% reaching 84.71 \% compared to the 81.49 \% of the IMU baseline model. We have also shown how bio-impedance can improve human activity recognition (HAR) directly through sensor fusion, reaching an average Macro F1 score of 89.57 \% (two modalities required for both training and inference) even if Bio-impedance alone has an average macro F1 score of 75.36 \%, which is outperformed by IMU alone. In addition, similar results were obtained in an extended study on lower body fitness activity classification, demonstrating the generalisability of our approach.Our findings underscore the potential of sensor fusion and contrastive learning as valuable tools for advancing fitness activity recognition, with bio-impedance playing a pivotal role in augmenting the capabilities of IMU-based systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
