人工智慧的應用：骨導麥克風及老年智慧手環

Abstract

人工智慧是從二十世紀中期在電腦及電腦程式語言創造出來後才開始的研究。電腦計算能力與平行處理使得人工智慧在某些特殊的領域優於人類。許多的類神經網路讓電腦能從資料學到規則。電腦每個迴圈都會學習一些，經過幾千幾百萬的迴圈他們就會學習到規則並做的比人好。 我們使用人工智慧的技術在下列應用：骨導麥克風及老年智慧手環。 骨導麥克風： 骨導麥克風是基於說話者頭骨的振動來產生語音，在使用骨導麥克風做傳輸語音時它們比正常的氣導麥克風表現出更好的抗噪能力。但因為骨導麥克風只能擷取語音信號的低頻部分，所以它們的頻率響應與氣導麥克風的頻率響應差異很大。當用骨導麥克風代替氣導麥克風時，我們可以在噪音抑制方面取得令人滿意的結果，但由於固體振動的性質，語音品質和理解度可能會降低。 骨導麥克風和氣導麥克風的不一致特性也會影響語音辦識的性能，並且使用骨導麥克風的語音數據重新創建新的語音辦識系統是不可行的。在這項研究中，我們提出了一種新穎的深度去噪自動編碼器方法來橋接骨導麥克風和氣導麥克風，以提高語音品質和可理解度，可以直接使用目前的語音辦識系統而不需重新創建新系統。實驗結果顯示，深度去噪自動編碼器的方法可以有效提高語音品質和可理解度在一些標準化評估指標。此外，我們提出的系統可以顯著提高語音辦識性能，在無噪音的條件下，相對字元錯誤率降低了48.28％（從14.50％降至7.50％）。在實際噪音環境下（噪音聲壓從61.7 dBA到73.9 dBA），我們提出的使用骨導麥克風加深度去噪自動編碼器優於氣導麥克風，相對字元錯誤率（骨導麥克風：9.13％和氣導麥克風：58.75％）減少84.46％。 老年智慧手環： 現代醫學使更多的人口能夠生存超過65歲。長者照護已成為唯一最相關的全球挑戰之一。有許多相關議題與監測及管理老年人的日常活動。在這個研究中，我們將檢測使用先進活動傳感平台在監控長者日常活動中的活動水平。它將有助於了解個別老年人的生活方式促進其安全並提高生活素質經由量測身體上的力量及獨立性。我們調查了可穿戴設備的要求用於老年人的技術，採用慣性測量傳感器。我們建議的系統包括每個老年人的可穿戴設備，物聯網接收器環境，智能警報，機器學習算法，以及與使用遠程網路的應用程序處理介面。我們提出的慣性測量傳感器應用的主要包括精確測量個人身體活動水平。

Artificial Intelligence (AI) study starts from the middle of 20 century after computers and computer languages are created. Computer calculation power and parallel processing make AI superior to human in specific domain. Many different neural networks make computers learn the rules from data. Computers learn a little in every loop. After thousands and millions of loops, they finally learn the rules and perform better than human. We use AI technologies in the following applications: bone-conducted microphone and senior smart band. Bone-Conducted Microphone (BCM): BCMs capture speech signals based on the vibrations of the talker’s skull, and they exhibit better noise-resistance capability than normal Air-Conducted Microphones (ACMs) when transmitting speech signals. Because BCMs only capture the low-frequency portion of speech signals, their frequency response is quite different from that of ACMs. When replacing an ACM with a BCM, we may obtain satisfactory results with respect to noise suppression, but the speech quality and intelligibility may be degraded owing to the nature of the solid vibration. The mismatched characteristics of BCM and ACM can also impact the Automatic Speech Recognition (ASR) performance, and it is infeasible to recreate a new ASR system using the voice data from BCMs. In this study, we propose a novel Deep-Denoising AutoEncoder (DDAE) approach to bridge BCM and ACM in order to improve speech quality and intelligibility, and the current ASR could be employed directly without recreating a new system. Experimental results first demonstrate that the DDAE approach can effectively improve speech quality and intelligibility based on standardized evaluation metrics. Moreover, our proposed system can significantly improve the ASR performance with a notable 48.28% relative Character Error Rate (CER) reduction (from 14.50% to 7.50%) under quiet conditions. In an actual noisy environment (sound pressure from 61.7 dBA to 73.9 dBA), our proposed system with a BCM outperforms an ACM, yielding an 84.46% reduction in the relative CER (our proposed system: 9.13% and ACM: 58.75%). Senior Smart Band: Modern medicine enables a larger segment of the population to survive beyond 65 years old. Senior care has become one of the single most relevant challenges globally. There are many relevant issues related to monitoring and managing the daily activities of senior citizens. In this study, we will examine the role of advanced activity sensor platform to monitor their daily activity levels. It will assist in understanding the lifestyle of the individual seniors to promote safety and improve the quality of life through measurement of physical strength and independence. We investigated the requirements of wearable technology for the seniors, employing Inertial Measurement Unit (IMU) sensor for senior care. Our proposed system includes a wearable device for each senior, Internet of Thing (IoT) receiver environment, smart alert, machine learning algorithm, and Application Processing Interface (API) with remote Internet access. The primary application of our proposed IMU sensor application includes precise measurement of individual physical activity level.