The extensive ubiquitous availability of sensors in smart devices and the
Internet of Things (IoT) has opened up the possibilities for implementing
sensor-based activity recognition. As opposed to traditional sensor time-series
processing and hand-engineered feature extraction, in light of deep learning's
proven effectiveness across various domains, numerous deep methods have been
explored to tackle the challenges in activity recognition, outperforming the
traditional signal processing and traditional machine learning approaches. In
this work, by performing extensive experimental studies on two human activity
recognition datasets, we investigate the performance of common deep learning
and machine learning approaches as well as different training mechanisms (such
as contrastive learning), and various feature representations extracted from
the sensor time-series data and measure their effectiveness for the human
activity recognition task.

通过在两个人体活动识别数据集上进行广泛的实验研究，我们调查了常见的深度学习和机器学习方法以及不同的训练机制（如对比学习）和从传感器时间序列数据中提取的各种特征表示的性能，以评估它们在人体活动识别任务中的有效性。