用於識別老年人、殘疾人和盲人的偵測系統

Detection System Employed to Recognize Elderly, Disabled, and Blind Persons

徐廷忠(Ting-Zhong Xu)

指導教授 ： 楊淳良

淡江大學/工學院/電機工程學系碩士班人工智慧物聯網組/碩士(2021年)

https://doi.org/10.6846/TKU.2021.00616

摘要

本論文以駝背老人、坐輪椅的殘疾人、拄著盲杖的盲人為對象，識別這些需要幫助的人群。本研究主要基於谷歌Teachable Machine平台的姿勢識別來訓練模型，該模型適用於識別老年人、殘疾人和盲人的個體姿勢。 並且，我們基於識別結果的信心分數(Confidence Score)分析了模型背後的數據，並用它來探索每一人群姿勢的差異。
所提出的系統對於姿態辨識模型訓練與姿態數據分析探討步驟有四步：
第一步：先從谷歌Teachable Machine平台中訓練週期次數(Epochs)、批量(Batch Size)、學習率(Learning Rate)可調參數來最佳化模型。
第二步：將老年人、殘疾人、盲人姿態透過Coral PoseNet數據化。
第三步：各別針對老年人、殘疾人、盲人數據化的資料加以統計與歸類。
第四步：則是以數據化來比較各族群差異與相異之處，並做小結來改善模型。
未來，通過整合17個關鍵點數據，每族群的識別率可以高於68%。如果該場域只需要識別老年人、殘疾人和盲人等有需要的群體與其他群體的二分法，識別率可以高達94%。

關鍵字

老年人 ； 殘疾人 ； 盲人 ； 姿態辨識 ； Coral Dev Board ； Teachable Machine

並列摘要

This thesis focuses on the elderly with hunchbacks, the disabled in wheelchairs, and the blind with a guide stick, to recognize the groups who need help. This research is mainly based on the posture recognition of the Google Teachable Machine platform to train the model, which acknowledges individual postures of the elderly, the disabled, and the blind. Moreover, we analyzed the data behind the model based on the confidence score of the recognized result and used it to explore the differences in the posture of each group.
The proposed system has four steps for training the posture recognition model and analyzing the posture data: The first is to optimize the model from the Epochs, Batch Size, and Learning Rate adjustable parameters in Teachable Machine. Secondly, the step is to digitize the posture of the elderly, the disabled, and the blind through Coral PoseNet. The third is to collect statistics and categorize the elderly, the disabled, and the blind. The final step is to compare the differences and dissimilarities of each group based on 17 keypoint data and make a summary to improve the model.
In the future, the recognition rate of each group can be higher than 68% by incorporating 17 keypoint data. If the field only needs to identify the dichotomy between the group in need like the elderly, disabled, and blind persons and another group, the recognition rate can reach 94% high.

並列關鍵字

Elderly ； Disabled ； Blind ； Pose Recognition ； Coral Dev Board ； Teachable Machine

參考文獻

[1]國家發展委員會，三階段年齡人口變化趨勢。取自https://www.ndc.gov.tw/Content_List.aspx?n=D527207EEEF59B9B

[2]國家發展委員會，高齡化時程。取自https://www.ndc.gov.tw/Content_List.aspx?n=695E69E28C6AC7F3

[3]衛生福利部統計處，2.3.1身心障礙者人數按類別及縣市別分。取自

https://dep.mohw.gov.tw/dos/cp-2976-61106-113.html

[4]V. Bazarevsky, I. Grishchenko, K. Raveendran, T. Zhu, F. Zhang, and M. Grundmann. (2020). BlazePose: On-device Real-time Body Pose tracking. Cornell University. Retrieved from https://arxiv.org/abs/2006.10204