利用可穿戴传感器和机器学习进行水果采摘者活动识别
Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning
AI总结：
针对水果采摘袋清空时机难以掌握的问题，本文利用可穿戴传感器时间序列数据，提出半监督标注与活动识别方法，比较集成模型和深度循环卷积神经网络，后者在真实数据集上达到86%准确率。
摘要：
本文提出了一种基于可穿戴传感器生成的时间序列数据来检测水果采摘者活动的新颖应用。在收获期间，水果采摘者将水果采摘到可穿戴袋中，并将这些袋子清空到位于果园中的收获箱里。一旦装满，这些箱子会被迅速运送到冷藏包装厂，以延长采摘水果的货架期。对于农民和管理者而言，了解采摘袋何时被清空，对于更有效地管理收获箱、最大限度缩短采摘水果暴露在高温下的时间（这会导致货架期缩短）非常重要。我们提出了一种利用可穿戴传感器和机器学习方法进行人体活动识别，以检测这些清空袋子事件的手段。我们开发了一种半监督的数据标注方法。研究构建了基于特征的机器学习集成模型和深度循环卷积神经网络，并在真实世界数据集上进行了测试。经比较，该神经网络达到了86%的检测准确率。
英文摘要：
In this paper we present a novel application of detecting fruit picker activities based on time series data generated from wearable sensors. During harvesting, fruit pickers pick fruit into wearable bags and empty these bags into harvesting bins located in the orchard. Once full, these bins are quickly transported to a cooled pack house to improve the shelf life of picked fruits. For farmers and managers, the knowledge of when a picker bag is emptied is important for managing harvesting bins more effectively to minimise the time the picked fruit is left out in the heat (resulting in reduced shelf life). We propose a means to detect these bag-emptying events using human activity recognition with wearable sensors and machine learning methods. We develop a semi-supervised approach to labelling the data. A feature-based machine learning ensemble model and a deep recurrent convolutional neural network are developed and tested on a real-world dataset. When compared, the neural network achieves 86% detection accuracy.
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。
