對年長者來說，若跌倒并導(dǎo)致受傷可不是開玩笑的；為此美國德州理工大學(xué)(Texas Tech University)在德州儀器(TI)的贊助下展開了一項(xiàng)研究，目標(biāo)是藉由分析年長者的姿勢與步伐，好在他們可能跌倒之前發(fā)出警告。 這項(xiàng)研究項(xiàng)目已經(jīng)在德州理工大學(xué)的健康科學(xué)中心(Health Science Center)征求自愿者，以進(jìn)行穿戴式無線感測裝置的最佳化，接下來將在美國首個(gè)大學(xué)附設(shè)的老人病學(xué)研究機(jī)構(gòu)──也就是德州理工大學(xué)老人病學(xué)教育與看護(hù)中心(Geriatric Education and Care Center)──進(jìn)行臨床實(shí)驗(yàn)。 “我們已經(jīng)嘗試過各種方式的感測裝置布置法，例如穿戴在腿部的慣性傳感器，以及鞋底內(nèi)含壓力傳感器的拖鞋；而德州儀器的低功耗微控制器與結(jié)合無線電的人體穿戴 MEMS 加速度計(jì)/陀螺儀──就像外接式的心律調(diào)整器──效果最好?！痹撗芯宽?xiàng)目的主持人、德州儀器電子電機(jī)教授Donald Lie表示。 經(jīng)過三年的開發(fā)時(shí)間，Lie的研究團(tuán)隊(duì)不只打造出人體穿戴式無線感測裝置，也研發(fā)了一套能無線監(jiān)測病患的PC軟件，所產(chǎn)生的算法能可靠偵測患者跌倒的方向。Lie表示，要分辨出患者是從床上、汽車座椅或是家中各處的家具上跌落，是很困難的。

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可偵測年長病患跌倒的無線感測裝置模塊原型，其小尺寸可以夾在皮帶上 接下來，德州理工大學(xué)的研究團(tuán)隊(duì)將藉由在該校老人病學(xué)教育與看護(hù)中心的病患身上進(jìn)行臨床實(shí)驗(yàn)，將其軟件分析效果更精細(xì)化，好偵測出跌倒之前的征兆，達(dá)到預(yù)防的效果。新開發(fā)出的無線MEMS感測裝置內(nèi)含加速度計(jì)、陀螺儀，可以夾在皮帶上，若是不配戴皮帶的患者或是女性，也可以夾在內(nèi)衣背后。 該裝置采用了TI的MSP430微控制器、CC2500射頻(RF)收發(fā)器，支持低功耗SimpliciTI網(wǎng)絡(luò)通訊協(xié)議，執(zhí)行以TI ez430-RF2500開發(fā)平臺開發(fā)的專屬軟件堆棧。TI院士Allen Bowling表示，此研究成果受到矚目的原因，是不只能偵測病患是否跌倒，還可能提供預(yù)防性警告：“藉由分析病患在跌倒之前的姿勢與步伐，我們希望能在病患跌倒之前發(fā)出警告，好讓他們能及時(shí)抓住東西或是坐下?！?而若無法阻止患者跌倒，該裝置也能立即發(fā)送無線訊號給照顧者；但該研究項(xiàng)目的目標(biāo)是將分析方法──目前是在醫(yī)療機(jī)構(gòu)負(fù)責(zé)監(jiān)控的PC上執(zhí)行──編碼入MSP430微控制器中，因此無論病患是否位于可接收無線訊號的范圍中，都能收到警告訊息。 此外研究團(tuán)隊(duì)也將針對其它與人體平衡相關(guān)的疾病患者進(jìn)行監(jiān)測，例如帕金森氏癥、失智癥以及癲癇等，期望也能提供這些患者預(yù)防性的警告訊息?！拔覀兿嘈胚@些研究項(xiàng)目能為老年人的臨床看護(hù)帶來很大進(jìn)展?！盠ie表示；其研究團(tuán)隊(duì)成員還包括Tam Nguyen、Steven Zupancic、 Andrew Dentino、Ron Banister與Tim Dallas等多位醫(yī)師。 編譯：Judith Cheng 本文授權(quán)編譯自EE Times，版權(quán)所有，謝絕轉(zhuǎn)載 參考英文原文：MEMS project aims to prevent elderly from falling，by R. Colin Johnson

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{pagination} MEMS project aims to prevent elderly from falling R. Colin Johnson PORTLAND, Ore.—The infamous 1980s television commercial that featured the tag line "I've fallen and I can't get up" became the butt of a thousand jokes. But for the elderly, susceptibility to falls and resulting injuries is no laughing matter. Now, a development effort at Texas Tech University, sponsored by Texas Instruments Inc., is taking aim at preventing falls by analyzing posture and gait to send warning alerts to the elderly before they fall. The project has already enlisted volunteers at Texas Tech's Health Science Center to perfect the wireless wearable sensor and is on track next for clinical trials at the first U.S. on-campus geriatric teaching facility, Texas Tech's Geriatric Education and Care Center. "We have tried all sorts of sensor placements, from leg-mounted inertial sensors to slippers with pressure sensors in their soles, but Texas Instruments' low-power microcontrollers and wireless radio combined with a torso-mounted MEMS accelerometer and gyro—like an external pacemaker—gives the best results," said lead scientist on the project, Texas Tech EE professor Donald Lie. After a three-year development effort, Lie's team has crafted not only the torso-mounted wireless sensor but also the software analytics running on a PC that wirelessly monitors patients, resulting in algorithms that can reliably detect falls regardless of in which direction, which Lie claims is difficult to differentiate from the many ways that people plop down into their beds, cars seats and the various pieces of furniture around the home. Next, the team is honing its software analytics even finer to detect pre-fall conditions in order to take preventative measures by virtue of clinical trials on real patients at the local Geriatric Education and Care Center where they have already installed the necessary wireless infrastructure. The MEMS sensor and wireless radio module is clipped on the belt, or for women not wearing a belt on the back of their bra, and contains a MEMS accelerometer, MEMS gyroscope, TI's MSP430 microcontroller and CC2500 radio frequency (RF) transceiver and uses the ultra-low-power SimpliciTI network protocol running a proprietary software stack developed on TI's ez430-RF2500 development platform. "We became interested in this project at Texas Tech, because it went beyond just fall detection, but aspired to preventative measures," said TI Fellow Allen Bowling. "By analyzing the dynamics of posture and gait--the way people are standing or walking before a fall—we hope to be able to send an alarm instructing them to grab hold of something or sit down before they fall." If the patient does end up falling, an alarm is sent wirelessly to a care provider, but the goal is to code the analytics—which today runs on a clinician-monitored PC—for the MSP430 microcontroller so alarms can be issued to patients regardless of whether they are in range of a wireless router or not. The team also has its sights on monitoring other vestibular (balance related) disorders and diseases including Parkinson's, dementia and epilepsy, in hopes of issuing warnings to those patients of impending episodes thereby enabling them to take preventative measures. "We believe that these projects will really make a significant difference in the clinical care of geriatric populations," said Lie, who credits his team of collaborators, including doctors Tam Nguyen, Steven Zupancic, Andrew Dentino, Ron Banister, and Tim Dallas.