1 簡介

—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 部分代碼

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包含訓練數(shù)據(jù)載體（每列）% k is number of centroids required 需要的質(zhì)心% % Output: r contains the result VQ codebook (k columns, one for each centroids)R含有結果VQ碼書（k列，為每個形心） 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 仿真結果

4 參考文獻

[1]亞森·艾則孜, 木尼拉·塔里甫. 基于連接數(shù)據(jù)分析和OSELM分類器的網(wǎng)絡入侵檢測系統(tǒng)[J]. 計算機應用研究, 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.?

博主簡介：擅長智能優(yōu)化算法、神經(jīng)網(wǎng)絡預測、信號處理、元胞自動機、圖像處理、路徑規(guī)劃、無人機等多種領域的Matlab仿真，相關matlab代碼問題可私信交流。

部分理論引用網(wǎng)絡文獻，若有侵權聯(lián)系博主刪除。