【情感识别】基于ELM、SOM分类器实现心率变异性信号情感识别附matlab代码

2024-04-29 11:49:48

1 简介

—Dimensional affect recognition is a challenging topic and current techniques do not yet provide the accuracy necessary for HCI applications. In this work we propose two new methods. The fifirst is a novel self-organizing model that learns from similarity between features and affects. This method produces a graphical representation of the multidimensional data which may assist the expert analysis. The second method uses extreme learning machines, an emerging artifificial neural network model. Aiming for minimum intrusiveness, we use only the heart rate variability, which can be recorded using a small set of sensors. The methods were validated with two datasets. The fifirst is composed of 16 sessions with different participants and was used to evaluate the models in a

classifification task. The second one was the publicly available Remote Collaborative and Affective Interaction (RECOLA) dataset, which was used for dimensional affect estimation. The performance evaluation used the kappa score, unweighted average recall and the concordance correlation coeffificient. The concordance coeffificient on the RECOLA test partition was 0.421 in arousal and 0.321 in valence. Results shows that our models outperform state-of-the-art models on the same data and provides new ways to analyze affective states.

2 部分代码

function r = vqlbg(d,k) % VQLBG Vector quantization using the Linde-Buzo-Gray algorithme 矢量量化% % Inputs: d contains training data vectors (one per column) d包含训练数据载体（每列）% k is number of centroids required 需要的质心% % Output: r contains the result VQ codebook (k columns, one for each centroids)R含有结果VQ码书（k列，为每个形心） e = .01; r = mean(d, 2); dpr = 10000; for i = 1:log2(k) r = [r*(1+e), r*(1-e)]; while (1 == 1) z = disteu(d, r); [m,ind] = min(z, [], 2); t = 0; for j = 1:2^i r(:, j) = mean(d(:, find(ind == j)), 2); x = disteu(d(:, find(ind == j)), r(:, j)); for q = 1:length(x) t = t + x(q); end end if (((dpr - t)/t) < e) break; else dpr = t; end end end

3 仿真结果

4 参考文献

[1]亚森·艾则孜, 木尼拉·塔里甫. 基于连接数据分析和OSELM分类器的网络入侵检测系统[J]. 计算机应用研究, 2017, 34(12):4.

[2] Bugnon L A , Rafael, Milone D H . transactions on affective computing 1 dimensional affect recognition from hrv: an approach based on supervised som and elm.

博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。

部分理论引用网络文献，若有侵权联系博主删除。

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