Promoting Occupancy Detection Accuracy Using On-Device Lifelong Learning
Promoting Occupancy Detection Accuracy Using On-Device Lifelong Learning
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DOI：
10.1109/jsen.2023.3260062
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发表时间：
2023-05
影响因子：
4.3
通讯作者：
Muhammad Emad-ud-din;Ya Wang
中科院分区：
文献类型：
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作者：
Muhammad Emad-ud-din;Ya Wang
我们最近开发的同步低能量电子斩波被动红外（SLEEPIR）传感器节点，使传统的被动红外（PIR）传感器的静态占用检测能力。机器学习（ML）算法基于从传感器节点本地收集的数据集报告占用率。虽然很有前途，但ML算法的检测精度取决于收集的数据集的多样性-前提是数据集包含各种各样的红外（IR）噪声和占用模式。因此，训练一个包含所有可能模式的通用ML模型是具有挑战性的。我们提出了一个有效的${K}$ -最近邻（KNN）占用分类器，增量适应新的数据从传感器。所提出的算法确保只学习相关的噪声和占用模式。事实上，训练观察收集在同一个传感器节点上进行推理，即使数据集的大小有限，也可以保持所提出的分类器的准确性。小数据集和KNN等架构都可以在资源受限的物联网（IoT）设备上执行训练和推理。因此，提出的设备上终身学习（ODLL）方法消除了对云ML模型更新的需要。该数据集是在两个月内针对两个不同的平面图收集的。结果表明，与静态训练的长短期记忆（LSTM）模型相比，平均占用准确率提高了20.8%。所提出的KNN模型提供了相当的检测精度，同时与基于LSTM的占用检测算法相比，在计算性能方面保持了更快的数量级。
Our recently developed synchronized low-energy electronically chopped passive infrared (SLEEPIR) sensor node enables the stationary occupancy detection capability of traditional passive infrared (PIR) sensors. A machine learning (ML) algorithm reports occupancy based on a locally collected dataset from the sensor node. Though promising, the ML algorithm’s detection accuracy depends on the diversity of the collected dataset—provided that the dataset contains a wide variety of infrared (IR) noise and occupancy patterns. Thus, it is challenging to train a universal ML model that contains all possible patterns. We propose an efficient ${K}$ -nearest neighbor (KNN) occupancy classifier that incrementally adapts to the novel data from the sensor. The proposed algorithm ensures that only the relevant noise and occupancy patterns are learned. The fact that training observations are gathered on the same sensor node where the inference is made keeps the proposed classifier accurate even with the bounded size of the dataset. A small dataset and an architecture like KNN both enable the training and inference to be executed on a resource-constrained Internet of Things (IoT) device. Thus, the proposed on-device lifelong learning (ODLL) approach eliminates the need for over-the-cloud ML model updates. The dataset was collected for two distinct floorplans over two months. Results indicate an average occupancy accuracy improvement of 20.8% compared to a statically trained long short-term memory (LSTM) model. The proposed KNN model delivers comparable detection accuracy while remaining orders of magnitude faster in terms of computational performance when compared to the LSTM-based occupancy detection algorithm.