Abstract
Wearable telecare and telehealth systems are those which can be worn on the human body and continuously monitor a user’s vital status. Even though these systems have already shown promise in applications for improving medical service quality and reducing medical costs, a short battery life significantly restricts the widespread use of these systems. Low-power technologies (a general name for technologies which use various approaches to reduce the power consumption of the associated electronics) can help alleviate this disadvantage of wearable telecare and telehealth systems. In this paper, we review recent developments and applications of low-power technologies in wearable telecare and telehealth systems, sorting the various approaches into two categories: hardware-based approaches and firmware-based approaches. This paper focuses on illustrating how to realize these approaches but does not provide a quantitative analysis of different approaches, since the intended applications of these approaches are quite different, hence numeric comparison is not meaningful. Given the proliferation of wearable telecare and telehealth systems, there will be a greater emphasis on the development of low-power technologies in this field.
Similar content being viewed by others
References
Barlow J, Singh D, Bayer S, Curry R. A systematic review of the benefits of home telecare for frail elderly people and those with long-term conditions. J Telemed Telecare. 2007; 13(4):172–9.
Mistry H. Systematic review of studies of the cost-effectiveness of telemedicine and telecare. Changes in the economic evidence over twenty years. J Telemed Telecare. 2012; 18(1):1–6.
Nyman SR, Victor CR. Use of personal call alarms among community-dwelling older people. Ageing Soc. 2014; 34(1):67–89.
Anliker U, Ward JA, Lukowicz P, TrÖster G, Dolveck F, Baer M, Keita F, Schenker EB, Catarsi F, Coluccini L, Belardinelli A, Shklarski D, Alon M, Hirt E, Schmid R, Vuskovic M. AMON: A wearable multiparameter medical monitoring and alert system. IEEE T Inf Technol Biomed. 2004; 8(4):415–27.
Schwickert L, Becker C, Lindemann U, Maréchal C, Bourke A, Chiari L, Helbostad JL, Zijlstra W, Aminian K, Todd C, Bandinelli S, Klenk J, Consortium F, the FMDCG. Fall detection with body-worn sensors: a systematic review. Z Gerontol Geriatr.2013; 46(8):706–19.
Wang C, Wang Q, Shi S. A distributed wireless body area network for medical supervision. Conf Proc IEEE Int Instrum Meas Technol Conf. 2012; 1:2612–6.
Shnayder V, Chen B-R, Lorincz K, Jones TRF, Welsh M. Sensor networks for medical care. http://www. eecs.harvard.edu/˜mdw/proj/codeblue. 2005. Accessed 31-Aug-2005.
Klasnja P, Pratt W. Healthcare in the pocket: mapping the space of mobile-phone health interventions. J Biomed Inform. 2012; 45(1):184–98.
Del Rosario MB, Wang K, Wang J, Liu Y, Brodie M, Delbaere K, LKovell NH, Lord SR, Redmond SJ. A comparison of activity classification in younger and older cohorts using a smartphone. Physiol Meas. 2014; 35(11):2269–86.
May CR, Finch TL, Cornford J, Exley C, Gately C, Kirk S, Jenkings K, Osbourne J, Robinson AL, Rogers A, Wilson R, Mair FS. Integrating telecare for chronic disease management in the community: what needs to be done? BMC Health Serv Res. 2011; doi: 10.1186/1472-6963-11-131.
Tamura T, Yoshimura T, Sekine M, Uchida M, Tanaka O. A wearable airbag to prevent fall injuries. IEEE T Inf Technol Biomed. 2009; 13(6):910–4.
Chan M, Estève D, Fourniols JY, Escriba C, Campo E. Smart wearable systems: current status and future challenges. Artif Intell Med. 2012; 56(3):137–56.
Igual R, Medrano C, Plaza I. Challenges, issues and trends in fall detection systems. Biomed Eng Online. 2013; doi: 10.1186/1475-925X-12-66.
Pantelopoulos A, Bourbakis NG. A survey on wearable sensorbased systems for health monitoring and prognosis. IEEE T Syst Man Cy C. 2010; 40(1):1–12.
Burns A, Greene BR, McGrath MJ, O’Shea TJ, Kuris B, Ayer SM, Stroiescu F, Cionca V. SHIMMER (TM) - A wireless sensor platform for noninvasive biomedical research. IEEE Sens J. 2010; 10(9):1527–34.
Bloom L, Eardley R, Geelhoed E, Manahan M, Ranganathan P. Investigating the relationship between battery life and user acceptance of dynamic, energy-aware interfaces on handhelds. In: Brewster S, Dunlop M, editors. Mobile Human-Computer Interaction - MobileHCI 2004. Berlin Heidelberg: Sprigner; 2004. pp. 13–24.
Alshurafa N, Eastwood JA, Nyamathi S, Liu JJ, Xu W, Ghasemzadeh H, Pourhomayoun M, Sarrafzadeh M. Improving compliance in a remote health monitoring system through smartphone battery optimization. IEEE J Biomed Health Inform. 2015; 19(1):57–63.
Bergmann JHM, McGregor AH. Body-worn sensor design: what do patients and clinicians want? Ann Biomed Eng. 2011; 39(9):2299–312.
Bodine K, Gemperle F. Effects of functionality on perceived comfort of wearables. Conf Proc Int Symp Wearable Comput. 2003; 1:57–60.
Hartholt KA, van Beeck EF, Polinder S, van der Velde N, van Lieshout EM, Panneman MJ, van der Cammen TJ, Patka P. Societal consequences of falls in the older population: injuries, healthcare costs, and long-term reduced quality of life. J Trauma. 2011; 71(3):748–53.
Yip M. Ultra-low-power circuits and systems for wearable and implantable medical devices. http://hdl.handle.net/1721.1/84902. 2013. Accessed 31-Dec-2013.
Paradiso JA, Starner T. Energy scavenging for mobile and wireless electronics. IEEE Pervas Comput. 2005; 4(1):18–27.
Piguet C. Low-power electronics design. CRC press; 2004
Horowitz M, Indermaur T, Gonzalez R. Low-power digital design. Conf Proc IEEE Symp Low Power Electron. 1994; 1:8–11.
Pillai P, Shin KG. Real-time dynamic voltage scaling for lowpower embedded operating systems. Conf Proc ACM Symp Oper Syst Princ. 2001; 35(5):89–102.
Albers S. Energy-efficient algorithms. Commun ACM. 2010; 53(5):86–96.
Benini L, Bogliolo A, De Micheli G. A survey of design techniques for system-level dynamic power management. IEEE T Vlsi Syst. 2000; 8(3):299–316.
Benini L, De Micheli G. System-level power optimization: techniques and tools. ACM T Des Autom Electron Syst. 2000; 5(2):115–92.
Alioto M. Ultra-low power VLSI circuit design demystified and explained: A tutorial. IEEE T Circuits-I. 2012; 59(1):3–29.
Zhuravlev S, Saez JC, Blagodurov S, Fedorova A, Prieto M. Survey of energy-cognizant scheduling techniques. IEEE T Parall Distr. 2013; 24(7):1447–64.
Sarpeshkar R. Universal principles for ultra low power and energy efficient design. IEEE T Circuits-II. 2012; 59(4):193–8.
Khateb F, Dabbous SBA, Vlassis S. A survey of nonconventional techniques for low-voltage low-power analog circuit design. Radioengineering. 2013; 22(2):415–27.
Ravanshad N, Rezaee-Dehsorkh H, Lotfi R, Lian Y. A levelcrossing based QRS-detection algorithm for wearable ECG sensors. IEEE J Biomed Health Inform. 2014; 18(1):183–92.
Braojos R, Mamaghanian H, Junior AD, Ansaloni G, Atienza D, RincÓn FJ, Murali S. Ultra-low power design of wearable cardiac monitoring systems. Conf Proc Annu Des Autom Conf. 2014; 1:1–6.
French B, Siewiorek DP, Smailagic A, Deisher M. Selective sampling strategies to conserve power in context aware devices. Conf Proc IEEE Int Symp Wearable Comput. 2007; 1:77–80.
Shih E, Guttag J. Reducing energy consumption of multichannel mobile medical monitoring algorithms. Conf Proc Int Workshop Syst Netw Support Health Care Assist Living Environ. 2008; doi: 10.1145/1515747.1515767.
Ebrazeh A, Mohseni P. A 14pJ/pulse-TX, 0.18nJ/b-RX, 100Mbps, channelized, IR-UWB transceiver for centimeter-to-meter range biotelemetry. Conf Proc Cust Integr Circuits Conf. 2014; 1:1–4.
Deepu CJ, Zhang X, Liew W-S, Wong DLT, Lian Y. An ECGon- Chip With 535 nW/Channel Integrated Lossless Data Compressor for Wireless Sensors. IEEE J Solid-St Circ. 2014; 49(11):2435–48.
Rofouei M, Farella E, Brunelli D, Sarrafzadeh M, Benini L. Battery-aware power management techniques for wearable haptic nodes. Conf Proc Int Conf Body Area Netw. 2010; doi: 10.1145/2221924.2221967.
Porcarelli D, Donati I, Nehani J, Brunelli D, Magno M, Benini L. Design and implementation of a multi sensors self sustainable wearable device. Conf Proc Eur Embed Des Educ Res Conf. 2014; 1:16–20.
Stäger M, Lukowicz P, TrÖster G. Power and accuracy tradeoffs in sound-based context recognition systems. Perv Mob Comput. 2007; 3(3):300–27.
Zappi P, Roggen D, Farella E, TrÖster G, Benini L. Network-level power-performance trade-off in wearable activity recognition: a dynamic sensor selection approach. ACM T Embed Comput Syst. 2012; 11(3):1–30.
Zappi P, Lombriser C, Stiefmeier T, Farella E, Roggen D, Benini L, TrÖster G. Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. In: Verdone R, editors. Wireless Sensor Networks. Bologna: Berlin Heidelberg: Springer; 2008. pp. 17–33.
Kitayoshi H, Sawaya K, Kuwano H. Ultra low power wireless ECG sensor tag with wearable antenna. Conf Proc IEEE Sens. 2013; 1:1–4.
Zhou C-C, Tu C-L, Gao Y, Wang F-X, Gong H-W, Lian P, He C, Ye X-S. A low-power, wireless, wrist-worn device for long time heart rate monitoring and fall detection. Conf Proc IEEE Int Conf Orange Technol. 2014; 1:33–6.
Yang K, Jiang H, Yang W, Mes F, Zhang C, Wang Z, Lin Q, Jia W. Lifetime tracing of cardiopulmonary sounds with lowpower sound sensor stick connected to wireless mobile network. Analog Integr Circ S. 2014; 81(3):623–34.
Analog Devices, ADXL 345 datasheet, http://www.analog.com/en/products/mems/mems-accelerometers/adxl345.html
Waber T, Pahl W, Schmidt M, Feiertag G, Stufler S, Dudek R, Leidl A. Flip-chip packaging of piezoresistive barometric pressure sensors. Conf Proc SPIE Conf Smart Sens Actuators MEMS VI. 2013; doi: doi:10.1117/12.2016459.
Kerckhof S, Standaert F-X, Peeters E. From new technologies to new solutions. In: Francillon A, Rohatgi P, editors. Smart card research and advanced applications. International Publishing: Springer; 2014. pp. 16–29.
Zoss AB, Kazerooni H, Chu A. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX). IEEE-ASMET Mech. 2006; 11(2):128–38.
Texas Instruments. CC1101 Low-power sub-1GHz RF transceiver. http://focus.ti.com/docs/prod/folders/print/cc1101. html. 2010. Accessed 2-Feb-2010.
Chen N, Kim S, van den Dungen W, Baggen CPMJ, Doornbos RMP. Fall detection system. WO Patent n. 2009138941. 2009.
Kim J-C, Kim K-S, Kim S. Wearable sensor system including optical 3-axis GRF sensor for joint torque estimation in realtime gait analysis. Conf Proc IEEE/ASME Int Conf Adv Intell Mechatron. 2014; 1:112–7.
Wang J, Redmond SJ, Voleno M, Narayanan MR, Wang N, Cerutti S, Lovell NH. Energy expenditure estimation during normal ambulation using triaxial accelerometry and barometric pressure. Physiol Meas. 2012; 33(11):1811–30.
Ning J. Fall detection application by using 3-axis accelerometer ADXL345. http://blog.ednchina.geo.eet-cn.com/uploadedn/ Blog/2009/7/1/32909b38-5e12-4a32-bd7e-19e75256ed35.pdf. 2009. Accessed 31-Dec-2009.
Broeders J-H. Wearable electronic devices monitor vital signs, activity level, and more. http://www.analog.com/library/analogdialogue/archives/48-12/wearable_electronics.html. Accessed 14-Mar-2015.
Roh T, Hong S, Yoo H-J. Wearable depression monitoring system with heart-rate variability. Conf Proc IEEE Eng Med Biol Soc. 2014; 1:562–5.
Shoaib M, Jha N, Verma N. A low-energy computation platform for data-driven biomedical monitoring algorithms. Conf Proc ACM/EDAC/IEEE Des Autom Conf. 2011; 1:591–6.
Park C, Chou PH, Bai Y, Matthews R, Hibbs A. An ultrawearable, wireless, low power ECG monitoring system. Conf Proc IEEE Biomed Circuits Syst Conf. 2006; 1:241–4.
Nemati E, Deen MJ, Mondal T. A wireless wearable ECG sensor for long-term applications. IEEE Commun Mag. 2012; 50(1):36–43.
Ren L, Zhang Q, Shi W. Low-power fall detection in homebased environments. Conf Proc ACM Int Workshop Perv Wirel Healthc. 2012; 1:39–44.
Led S, Fernández J, Serrano L. Design of a wearable device for ECG continuous monitoring using wireless technology. Conf Proc IEEE Eng Med Biol Soc. 2004; 1:3318–21.
Weiss A, Herman T, Giladi N, Hausdorff JM. Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days. PLoS One. 2014; doi: 10.1371/journal.pone.0096675.
Liu J, Priyantha B, Hart T, Ramos HS, Loureiro AAF, Wang Q. Energy efficient GPS sensing with cloud offloading. Conf Proc ACM Conf Embed Netw Sens Syst. 2012; 1:85–98.
Lanata A, Valenza G, Nardelli M, Gentili C, Scilingo EP. Complexity index from a personalized wearable monitoring system for assessing remission in mental health. IEEE J Biomed Health Inform. 2014; 19(1):132–9.
Redmond SJ, Lovell NH, Yang GZ, Horsch A, Lukowicz P, Murrugarra L, Marschollek M. What does big data mean for wearable sensor systems? contribution of the IMIA wearablesensors in healthcare WG. Yearb Med Inform. 2014; 9(1):135–42.
Lord SR, Sherrington C, Menz HB, Close JC. Falls in older people: risk factors and strategies for prevention. 2nd ed. Cambridge University Press;2007.
Wang C, Narayanan MR, Lord SR, Redmond SJ, Lovell NH. A low-power fall detection algorithm based on triaxial acceleration and barometric pressure. Conf Proc IEEE Eng Med Biol Soc. 2014; 1:570–3.
Hong JO, Yuen SGJ. Wearable heart rate monitor. US Patent n. 20140275852. 2014.
Yuan J, Tan KK, Lee TH, Koh GCH. Power-efficient interruptdriven algorithms for fall detection and classification of activities of daily living. IEEE Sens J. 2015; 15(3):1377–87.
Mubashir M, Shao L, Seed L. A survey on fall detection: principles and approaches. Neurocomputing. 2013; 100:144–52.
Ghasemzadeh H, Jafari R. Ultra low-power signal processing in wearable monitoring systems: a tiered screening architecture with optimal bit resolution. ACM T Embed Comput Syst. 2013; doi: 10.1145/2501626.2501636.
Stäger M, Lukowicz P, Perera N, von Büren T, TrÖster G, Starner T. Soundbutton: design of a low power wearable audio classification system. Conf Proc Int Symp Wearable Comput. 2003; 1:1–12.
Lorincz K, Chen B-R, Challen GW, Chowdhury AR, Patel S, Bonato P, Welsh M. Mercury: a wearable sensor network platform for high-fidelity motion analysis. Conf Proc ACM Conf Embed Netw Sens Syst. 2009; 1:183–96.
Panuccio P, Ghasemzadeh H, Fortino G, Jafari R. Power-aware action recognition with optimal sensor selection: an AdaBoost driven distributed template matching approach. Conf Proc ACM Workshop Mob Syst Appl Serv Healthc. 2011; doi: 10.1145/2064942.2064950.
Leuenberger K, Gassert R. Low-power sensor module for long-term activity monitoring. Conf Proc IEEE Eng Med Biol Soc. 2011; 1:2237–41.
Fraternali F, Rofouei M, Alshurafa N, Ghasemzadeh H, Benini L, Sarrafzadeh M. Opportunistic hierarchical classification for power optimization in wearable movement monitoring systems. Conf Proc IEEE Int Symp Ind Embed Syst. 2012; 1:102–11.
Ghasemzadeh H, Jafari R. An ultra low power granular decision making using cross correlation: minimizing signal segments for template matching. Conf Proc IEEE/ACM Int Conf Cyber-Physical Syst. 2011; 1:77–86.
Srinivasan V, Phan T. An accurate two-tier classifier for efficient duty-cycling of smartphone activity recognition systems. Conf Proc Int Workshop Sens Appl Mob Phones. 2012; doi: 10.1145/2389148.2389159.
Sueppel BE, Mortara DW. Low power pulse oximeter. US Patent n. 6697655. 2004.
Yan L, Zhong L, Jha NK. Energy comparison and optimization of wireless body-area network technologies. Conf Proc Int Conf Body Area Netw. 2007; 1:1–8.
Ganti RK, Jayachandran P, Abdelzaher TF, Stankovic JA. SATIRE: a software architecture for smart AtTIRE. Conf Proc Int Conf Mob Syst Appl Serv.2006; 1:110–23.
Benbasat AY, Paradiso JA. A framework for the automated generation of power-efficient classifiers for embedded sensor nodes. Conf Proc Int Conf Embed Netw Sens Syst. 2007; doi: 10.1145/1322263.1322285.
Bagalà F, Becker C, Cappello A, Chiari L, Aminian K, Hausdorff JM, Zijlstra W, Klenk J. Evaluation of accelerometerbased fall detection algorithms on real-world falls. PLoS One. 2012; doi: 10.1371/journal.pone.0037062.
Mirchevska V, Luštrek M, Gams M. Combining domain knowledge and machine learning for robust fall detection. Expert Syst. 2014; 31(2):163–75.
Yuwono M, Moulton BD, Su SW, Celler BG, Nguyen HT. Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems. Biomed Eng Online. 2012; doi: 10.1186/1475-925X-11-9.
Rescio G, Leone A, Siciliano P. Supervised Expert system for wearable MEMS accelerometer-based fall detector. J Sens. 2013; doi: 10.1155/2013/254629.
Bharatula NB, Lukowicz P, TrÖster G. Functionality-powerpackaging considerations in context aware wearable systems. Pers Ubiquit Comput. 2008; 12(2):123–41.
Casamassima F, Farella E, Benini L. Context aware power management for motion-sensing body area network nodes. Conf Proc Des Autom Test Eur Conf Exhib. 2014; doi: 10.7873/DATE.2014.183.
Shih EI, Shoeb AH, Guttag JV. Sensor selection for energyefficient ambulatory medical monitoring. Conf Proc Int Conf Mob Syst Appl Serv. 2009; 1:347–58.
Ghasemzadeh H, Amini N, Sarrafzadeh M. Energy-efficient signal processing in wearable embedded systems: an optimal feature selection approach. Conf Proc ACM/IEEE Int Symp Low Power Electron Des. 2012; 1:357–62.
Imtiaz SA, Rodriguez-Villegas E. A low computational cost algorithm for REM sleep detection using single channel EEG. Ann Biomed Eng. 2014; 42(11):2344–59.
Lee KH, Kung S-Y, Verma N. Low-energy formulations of support vector machine kernel functions for biomedical sensor applications. J Signal Process Sys. 2012; 69(3):339–49.
Trakimas M, Sonkusale SR. An adaptive resolution asynchronous ADC architecture for data compression in energy constrained sensing applications. IEEE T Circuits-I. 2011; 58(5):921–34.
Watkins A, Mudhireddy VN, Wang H, Tragoudas S. Adaptive compressive sensing for low power wireless sensors. Conf Proc Great Lakes Symp VLSI. 2014; doi: 10.1145/2591513.2591537.
Dixon AMR, Allstot EG, Gangopadhyay D, Allstot DJ. Compressed sensing system considerations for ECG and EMG wireless biosensors. IEEE T Circuits Syst. 2012; 6(2):156–66.
Imtiaz SA, Casson AJ, Rodriguez-Villegas E. Compression in wearable sensor nodes: impacts of node topology. IEEE T Biomed Eng. 2014; 61(4):1080–90.
Ullah S, Khan P, Ullah N, Saleem S, Higgins H, Kwak KS. A review of wireless body area networks for medical applications. Int J Commun Netw Syst Sci. 2010; 2(8):797–803.
Benocci M, Tacconi C, Farella E, Benini L, Chiari L, Vanzago L. Accelerometer-based fall detection using optimized ZigBee data streaming. Microelectron J. 2010; 41(11):703–10.
Natarajan A, Motani M, de Silva B, Yap K-K, Chua KC. Investigating network architectures for body sensor networks. Conf Proc ACM Int Workshop Syst Netw Support Healthc Assist Living Environ. 2007; doi: 10.1145/1248054.1248061.
Otal B, Alonso L, Verikoukis C. Highly reliable energy-saving MAC for wireless body sensor networks in healthcare systems. IEEE J Sel Area Comm. 2009; 27(4):553–65.
Li H, Tan J. An ultra-low-power medium access control protocol for body sensor network. Conf Proc IEEE Eng Med Biol Soc. 2005; 1:2451–4.
Xiao S, Dhamdhere A, Sivaraman V, Burdett A. Transmission power control in body area sensor networks for healthcare monitoring. IEEE J Sel Area Comm. 2009; 27(1):37–48.
Dhamdhere A, Sivaraman V, Burdett A. Experiments in adaptive power control for truly wearable biomedical sensor devices. Conf Proc Int Symp Parallel Distrib Process Appl. 2008; 1:919–25.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, C., Lu, W., Narayanan, M.R. et al. Low-power technologies for wearable telecare and telehealth systems: A review. Biomed. Eng. Lett. 5, 1–9 (2015). https://doi.org/10.1007/s13534-015-0174-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13534-015-0174-2