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摘要: 提出一種基于近鄰成分分析(Neighbourhood component analysis, NCA)、L2正則化和隨機配置網絡(Stochastic configuration networks, SCNs)的輕量型人體行為識別學習模型. 首先, 針對人體行為特征集維數過高且可分性差的問題, 利用NCA從特征集中選擇高相關性特征子集, 進而提高模型建模計算過程的輕量性和識別精度. 其次, 針對SCNs隱含層節點過多時容易出現過擬合的問題, 采用L2正則化方法增強SCNs的泛化能力, 同時利用監督機制約束產生隱含層參數的方法, 極大地提高了SCNs模型的輕量性. 最后, 將所提NCA?L2?SCNs學習模型在UCI HAR特征集上進行驗證, 實驗結果表明, 相比于其他模型, 本文所提輕量型模型對于人體行為識別具有更好的識別精度和更快的建模速度.Abstract: In the past few decades, smartphone-based human activity recognition research has played an important role in many fields, including smart buildings, healthcare, and the military. However, the CPU and storage space of smartphones are very limited, so developing a lightweight human activity recognition learning model has become a research focus and hot spot in this field. To address the abovementioned problems, this paper proposed a lightweight human activity recognition learning model based on the nearest neighbor component analysis (NCA), L2 regularization, and stochastic configuration networks (SCNs). In the proposed model, aiming first at the problem of high dimension and poor separability exhibited by the human activity data, NCA was used to select a subset of highly relevant data from the dataset to improve the lightness of calculation using the learning algorithm in the modeling process and recognition accuracy of the established model. Second, to prevent the occurrence of overfitting when there are too many hidden layer nodes in SCNs, the L2 regularization method was adopted to enhance the generalization ability of SCNs. At the same time, the method of using the supervision mechanism to restrict the generation of hidden layer parameters greatly improved the lightness of the SCNs model. Finally, the proposed learning model and other learning models were verified experimentally on the UCI human activity recognition dataset. Experimental results show that compared with SCNs, the proposed L2?SCNs model reduces the lightness of the number of parameters by 20% and helps improve the accuracy of the model. The introduction of the NCA method has greatly facilitated the recognition accuracy and lightness (modeling time) of the L2?SCNs model, increasing by 3.41% and 70.24%, respectively. Moreover, compared with other state-of-the-art models, such as the support vector machine and long short-term memory network, the proposed model achieves the best recognition accuracy of 97.48% in the shortest time. To sum up, the model proposed herein is a lightweight human activity recognition model with exceptional recognition accuracy and a fast modeling speed.
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圖 2 使用L2正則化前后SCNs的RMSE對比圖
Figure 2. RMSE comparison of SCNs before and after L2 regularization
表 1 不同算法的計算復雜度對比
Table 1. Comparison of the computational complexity of different algorithms
Algorithms Computational complexity SCNs $O( {{m^3} + 2 \times 561 \times m} )$ L2?SCNs $ O( {{2 \mathord{\left/ {\vphantom {2 3}} \right. } 3}({D^3}) + 2 \times 561 \times m} ) $ NCA?L2?SCNs $ O( {{2 \mathord{\left/ {\vphantom {2 3}} \right. } 3}({D^3}) + 2 \times 111 \times m} ) $ 
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表 2 使用NCA特征選擇前后L2?SCNs模型結果對比
Table 2. Comparison of L2?SCNs model results before and after using the NCA feature selection
Feature dimension Modeling time/s Average accuracy/% Minimum accuracy/% Maximum accuracy/% UCI feature set(561) 29.85 94.27 94.10 94.30 NCA(111) 18.03 97.48 97.05 97.93 NCA(94) 19.88 97.19 96.61 97.49
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表 3 五種方法在UCI特征集上的對比
Table 3. Comparison of five methods on the UCI feature set
Model Average accuracy/% Maximum accuracy/% Minimum accuracy/% Modeling time/s SVM 92.77 92.77 92.77 19.41 LSTM 93.35 94.88 92.57 529 SCNs 94.18 94.27 94.06 60.59 L2?SCNs 94.27 94.30 94.10 29.85 NCA?L2?SCNs 97.48 97.93 97.05 18.03
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表 4 NCA?L2?SCNs模型識別結果混淆矩陣
Table 4. Confusion matrix of NCA?L2?SCNs model recognition results
Predicted class Actual class Walking Upstairs Downstairs Sitting Standing Lying Walking 494 23 0 0 0 0 Upstairs 1 443 4 4 0 0 Downstairs 0 2 415 0 0 0 Sitting 1 3 0 466 1 0 Standing 0 0 1 20 531 2 Lying 0 0 0 1 0 535
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