可穿戴設(shè)備應及時收集數(shù)據(jù)，切身考慮用戶需求，只有這樣，產(chǎn)業(yè)才能實現(xiàn)真正的商業(yè)化發(fā)展。

近日，由工業(yè)和信息化部人才交流中心主辦，江北新區(qū)IC智慧谷頂山產(chǎn)業(yè)園協(xié)辦的“芯動力”人才發(fā)展計劃國際名家講堂系列最新活動在南京浦口區(qū)舉辦。作為活動的全程支持媒體，鎂客網(wǎng)受邀對本次講座的專家導師瑞士洛桑聯(lián)邦理工學院副教授David Atienza博士進行了一次專訪。

左起：工信部IC Power產(chǎn)業(yè)合作組王喆、洛桑聯(lián)邦理工學院David Atienza博士、鎂客網(wǎng)主編王剛

David Atienza博士表示，在過去幾年中，可穿戴設(shè)備的發(fā)展方向有所跑偏，因為這些設(shè)備研發(fā)商并沒有實際的去測量或收取用戶反饋數(shù)據(jù)，導致了產(chǎn)品的不適用性和無法給用戶安全感。未來，我們的可穿戴設(shè)備應該與物聯(lián)網(wǎng)緊密連接，并切身考慮用戶需求，只有這樣，產(chǎn)業(yè)才能實現(xiàn)真正的商業(yè)化發(fā)展。

以下是鎂客網(wǎng)對David Atienza博士的專訪實錄：

鎂客網(wǎng)：David 您好，我們了解到，您的研究主要是針對“高性能多處理器系統(tǒng)級芯片（MPSOC）和低功耗嵌入式系統(tǒng)的設(shè)計方法”的，包括針對2D和3D MPSoCs的新型熱感知設(shè)計，以及針對無線人體傳感器網(wǎng)絡(luò)，動態(tài)內(nèi)存管理和互連層次結(jié)構(gòu)優(yōu)化的設(shè)計方法和架構(gòu)?？梢哉埬冉榻B一下您在可穿戴設(shè)備的功耗和系統(tǒng)優(yōu)化方面取得了哪些最新的進展嗎？

David：近期，我們已經(jīng)在EPFL的嵌入式系統(tǒng)實驗室（ESL）中取得了非常重要的進展了。首先，我們證明了近閾值多核心計算架構(gòu)是更加節(jié)能的，它能使單核微控制器能夠通過處理下一代智能型可穿戴設(shè)備的復雜生物信號，甚至可以在穿戴式系統(tǒng)中運行機器學習算法，而不用將所有數(shù)據(jù)發(fā)送到遠程云計算系統(tǒng)中。此外，在ESL的系統(tǒng)層面，我們證明了壓縮感應是可應用于基于ECG監(jiān)測下的可穿戴設(shè)備的，它可以顯著的節(jié)省我們在檢測生物信號和采樣所需要耗費的精力。

鎂客網(wǎng)：對于可穿戴設(shè)備的商業(yè)化前景，您認為目前哪些方向的應用已經(jīng)成熟或具有可預見的成熟市場環(huán)境？

David：我認為，在過去幾年時間里，面向健康和消費者市場的大多數(shù)現(xiàn)有可穿戴設(shè)備的應用方向并不是很正確的，因為這些設(shè)備缺乏對用戶的測量或用戶提供的反饋數(shù)據(jù)及臨床驗證，這也是大多數(shù)不愿接受這些設(shè)備的原因之一。但我認為，這一領(lǐng)域的產(chǎn)品正在迅速朝健康、可靠、經(jīng)認證的方向發(fā)展，在系統(tǒng)保障用戶隱私和健康的前提下，面向健康和消費者市場的可穿戴設(shè)備是可具有成熟的市場的。

鎂客網(wǎng)：我們一直在提“可穿戴設(shè)備”的概念，前幾年這一類型的產(chǎn)品也大紅大紫過，其中最為突出的就是“智能手環(huán)”。但是它們卻迅速沒落了，全球出貨量大幅下滑，生廠商也大批倒閉或重組，您認為出現(xiàn)這種局面的原因有哪些？這其中除了它的實用性不足之外，當下這些產(chǎn)品在技術(shù)方面有哪些是急需改進的？

David：其實，當下可穿戴設(shè)備產(chǎn)業(yè)下滑的情況與我之前的預期是一致，以“智能手環(huán)”這一領(lǐng)域為例，大多數(shù)的產(chǎn)品是缺乏臨床驗證的，這使得很多用戶對手環(huán)系統(tǒng)給出的數(shù)據(jù)持質(zhì)疑態(tài)度，例如完成的步驟數(shù)量或每天消耗的熱量等，這也是此類產(chǎn)品銷售量不理想的重要原因之一。

不過，如果這些設(shè)備商可以很好的認識到這一點并對他們的產(chǎn)品進行改進的話，那么我認為這一領(lǐng)域還是很有發(fā)展?jié)摿Φ?。此外?strong>用戶界面方面，研發(fā)商應該多加考慮產(chǎn)品的適用和便捷性，并盡力使產(chǎn)品具備手機不可替代的功能，才有可能無讓智能手環(huán)真正、穩(wěn)定的商業(yè)化。

鎂客網(wǎng)：那么，您認為可穿戴設(shè)備是有必要作為單獨產(chǎn)品出現(xiàn)，還是作為日常用品的一個特性出現(xiàn)呢？

David：我認為，隨著智能手機的普及，可穿戴設(shè)備是應該并將會成為我們?nèi)粘Ｉ钏璧臇|西的，這只是一個時間問題。等到可穿戴設(shè)備擁有它們真正應該具備的功能時，人們就不會把它們當做一個“有趣的小工具”，而是將它們視為可提高我們生活質(zhì)量的必備用品。

鎂客網(wǎng)：您是怎樣看待可穿戴設(shè)備和物聯(lián)網(wǎng)之間的關(guān)系的？

David：可穿戴設(shè)備是物聯(lián)網(wǎng)中的一個關(guān)鍵組成部分，對于我來說，它們就是物聯(lián)網(wǎng)的基礎(chǔ)，是接物聯(lián)網(wǎng)和人類的第一個環(huán)節(jié)。因此，IoT設(shè)備的首次集成、交互是開始于具有可靠、舒適性的可穿戴設(shè)備的，進而才與其他IoT設(shè)備進行交互，最終讓智能城市、智能建筑、工業(yè)4.0等成為現(xiàn)實。

鎂客網(wǎng)：我們注意到，您在課題中特別提到了“特征提取，機器學習和數(shù)據(jù)記錄”，那么您認為，目前可穿戴設(shè)備對數(shù)據(jù)分析的準確性如何？距離實際應用還要多久？

David：我要強調(diào)的是，可穿戴設(shè)備從概念的提出到現(xiàn)在取得可在運行時提取生物信號的復雜功能這一重大進展僅有幾年的時間，因此，這是一個新的領(lǐng)域。而讓可穿戴設(shè)備“聰明”起來是取決于真正適用的用戶和應用場景的，例如，我們近期已經(jīng)在ESL-EPFL中證實，在心血管監(jiān)測中，將可能患有心律不齊疾病的病人的ECG（心電圖）輸入設(shè)備后，其識別的正確率可達到95-96％。因此，我認為這一新的可穿戴設(shè)備系列（包括機載機）正在快速準備就緒，以便在獲得FDA（食品藥品監(jiān)督管理局）認證或CE標志（一種安全認證標志）后被普通用戶使用?？梢詷酚^地認為，這一領(lǐng)域的產(chǎn)品是可以在未來幾年內(nèi)在市場上全面鋪開的。

另一方面，像腦電圖分析這樣的領(lǐng)域，其在開發(fā)嵌入式機器學習方面有著很多的難題，因此，只有通過與云計算服務相連接，才能在短期內(nèi)讓可穿戴設(shè)備可以在機器學習算法上運行起來，從而為可穿戴設(shè)備提供正確的指示。

鎂客網(wǎng)：在您的設(shè)想中，終極的可穿戴設(shè)備應用場景，是怎樣的？

David：在我看來，終極的可穿戴設(shè)備是真正“聰明”、可自主供電的，并且，它們應該是可適用于每一個人。此外，這些可穿戴設(shè)備應該可以給予用戶足夠的安全感。

所以，我所預計的終極的可穿戴設(shè)備在形態(tài)上可能就是一個簡單的小貼紙，只要貼在皮膚上就可以直接與大腦溝通、交流，不需要我們輸入一個字。雖然這對于在當下的技術(shù)研發(fā)人員來說是極具挑戰(zhàn)的，但我相信在未來，這一定會成為現(xiàn)實。

附：（本次采訪英文原稿）

1, Can you tell us something about the latest developments you have made in the power consumption and system optimization of wearable devices?

In my Embedded Systems Laboratory (ESL) at EPFL, we have made very important recent developments that prove that near-threshold multi-core computing architectures are more energy-efficient that single-core microcontrollers for complex bio-signal processing in next-generation smart wearable devices, and that they can be even used to run machine learning algorithms on-board in wearable systems instead of sending all the data to remote cloud computing systems (which requires a lot more energy than computing on-board actually). Also, at the system level at ESL we have been the first ones to demonstrate that compressive sensing can be used in ECG-based monitoring in wearables to dramatically reduce the energy spent in bio-signal sensing and sampling.

2, For the prospects of the commercialization of wearable device, do you think the current direction of the application have matured or have a predictable mature market environment?

I believe the application direction of most of current wearables in the last years (oriented towards wellness and consumer markets) is not the right application to make a difference, as these devices lack clinical validation on what they measure or provide as feedback to the user. Therefore, many people start being reluctant to accept them any longer, but I think this area of products is rapidly evolving towards truly health-reliable certified devices, and it will have a mature market environment in preventive (and personalized) healthcare if the systems are validated well and do not bother the user (i.e., we should not realize they are there).

3, We have been talking about the concept of "wearable equipment" and this kind of product also flourished a few years ago, one of the most popular is the "smart bracelet." But they are declining rapidly, the global shipments fell sharply and many manufacturers also closed down or went through reorganization. What makes this happen? Which point of this product is urgently needed to improve in addition to its lack of practical use?

This situation is very much aligned with my previous answer because “smart bracelet” are one clear example of wearables where the lack of validation has made many customers believe that the current systems are not reliable in what they indicate (e.g., amount of steps done or calories spent per day, etc.) so do not buy them anymore. Thus, this is one clear example where the real validation and adaptability of the devices, as these devices lack clinical validation on what they measure or provide as feedback to the user. Therefore, many people start being reluctant to use them any longer. However, I think this family of products will gain ground again if the devices are well tuned to be reliable. On the other hand, a key point that needs to be improved is the user interface, as it is one of the main limitations for a practical use (e.g., how are you going to write an e-mail or reply in an elaborated way to a message that somebody has sent you?). Until the smart bracelets (or smartwatches) are not able to remove the need for a phone completely, they will not reach a real stable commercial success.

4, Just continue, do you think that wearable devices should be used as a separate product, or as a feature of daily use?

I believe wearables should (and will) become a (normal) feature of our daily life, as the smartphone has done in the end. It is just a matter of time and finding the right features for them so that people do not see them as “funny gadgets” but rather elements that improve our quality of life.

5, What about the relationship between wearable devices and IoT?

Wearable devices are a key component in the idea of IoT because for me they are at the foundations of IoT. Indeed, they must create the first link between IoT and human beings by allowing the other connected things (not in contact with our bodies) to understand what we feel or need or want. So the first integration of IoT devices starts on having reliable (and comfortable) wearable devices that can interact with the other IoT devices to make smart cities, smart building or appealing Industry 4.0 environments (to mention a few) interacting with humans.

6, We note that you specifically mentioned the "feature extraction, machine learning and data records" in your subject, so I want to ask how accuracy the data analysis result of wearable device is and how long it can be practically adapted?

Note that it is already a major progress (only done in the couple of years) that wearables can run machine learning by extracting at run-time complex features from biological signals. So this is a new field to make wearable devices truly adaptive to the person wearing them (so “smart” overall) and the current accuracy that these devices can reach depends on the application. For instance, in cardiovascular monitoring, the possible ECG inputs can be identified as correct or having a possible arrhythmia in the order of 95-96%, as we have proved recently at ESL-EPFL. So this new family of wearable devices including on-board machine are getting ready fast to be use in the general audience (after passing the corresponding FDA or CE mark, depending on the market in US or Europe). So I am optimist that we can have them on the market in this area in the next couple of years. On the other hand, other areas like EEG analysis are having more difficulties to develop embedded machine learning, so wearables having this support will only be deployed in the short term (4-5 years in my view) by connecting them to cloud computing services, where these machine learning algorithms will be running on the background and providing the indications to the wearables to adapt to their results.

7, Can you describe how the ultimate wearable device application scene will be in your vision?

In my mind the ultimate wearable devices will need to be really smart, autonomous (do not need any battery) and able to adapt to each person. Moreover, they will not be disturbing us at all (so almost to the point of being almost “transparent”). Therefore, in my view they could (eventually) have the shape of a simple tiny sticker that we put on our skin and can interact with use by communicating directly with the brain without us pronouncing a word. I know this is really challenging (or essentially impossible) with our current technology, but I am confident that we will progress enough to make it happen in the future.

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