评估时间序列数据中采样不规则性的影响：以人类活动识别为案例研究
Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study
AI总结：
本研究通过模拟时间戳变化和随机数据丢失，评估采样不规则性对人类活动识别的影响，发现现有离散和连续时间神经网络均难以有效应对，凸显了开发稳健处理不规则采样新方法的必要性。
摘要：
人类活动识别（HAR）理想情况下依赖于以规则间隔采样的可穿戴或环境传感器数据，从而能够使用针对一致时间序列数据优化的标准神经网络模型作为输入。然而，现实世界的传感器数据常常由于硬件限制、节能措施或通信延迟等原因表现出不规则采样，这对已部署的静态HAR模型构成挑战。本研究通过两种方法模拟不规则数据来评估采样不规则性对HAR的影响：在采样间隔中引入轻微不一致（时间戳变化）以模拟传感器抖动，以及随机移除数据点（随机丢弃）以模拟因数据包丢失或传感器故障导致的缺失值。我们在三个公开数据集上评估了离散时间神经网络和连续时间神经网络（后者专为处理连续时间数据而设计）。我们证明，时间戳变化不会显著影响离散时间神经网络的性能，而连续时间神经网络在应对不规则采样带来的挑战方面也无效，这可能是由于在缺失数据情况下建模复杂时间模式的局限性。我们的研究结果强调了需要能够稳健处理时间序列数据（如人类活动识别中的读数）中采样不规则性的新模型或方法，为该领域的未来研究铺平道路。
英文摘要：
Human activity recognition (HAR) ideally relies on data from wearable or environment-instrumented sensors sampled at regular intervals, enabling standard neural network models optimized for consistent time-series data as input. However, real-world sensor data often exhibits irregular sampling due to, for example, hardware constraints, power-saving measures, or communication delays, posing challenges for deployed static HAR models. This study assesses the impact of sampling irregularities on HAR by simulating irregular data through two methods: introducing slight inconsistencies in sampling intervals (timestamp variations) to mimic sensor jitter, and randomly removing data points (random dropout) to simulate missing values due to packet loss or sensor failure. We evaluate both discrete-time neural networks and continuous-time neural networks, which are designed to handle continuous-time data, on three public datasets. We demonstrate that timestamp variations do not significantly affect the performance of discrete-time neural networks, and the continuous-time neural network is also ineffective in addressing the challenges posed by irregular sampling, possibly due to limitations in modeling complex temporal patterns with missing data. Our findings underscore the necessity for new models or approaches that can robustly handle sampling irregularity in time-series data, like the reading in human activity recognition, paving the way for future research in this domain.
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。
