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.

翻译：自动且精确的健身活动识别在从促进健康生活方式到个性化预防性医疗保健等多个方面都具有益处。虽然惯性测量单元（IMU）是目前主流的健身追踪模态，但通过iMove，我们展示了生物阻抗可以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。为评估我们的方法，我们进行了一项实验，包含十名受试者在五天内完成的六种上肢健身活动，以收集来自双腕生物阻抗和左腕IMU的同步数据。对比学习框架利用这两种模态训练了一个更优的仅使用IMU的分类模型，其中生物阻抗仅在训练阶段需要，这使得单IMU输入的平均宏F1分数相比IMU基线模型的81.49%提高了3.22%，达到84.71%。我们还展示了生物阻抗如何通过传感器融合直接提升人类活动识别（HAR）性能，即使生物阻抗单独的平均宏F1分数为75.36%（低于单独使用IMU），但融合后平均宏F1分数达到了89.57%（训练和推理均需两种模态）。此外，在下肢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的系统能力方面发挥着关键作用。