Abstract
Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.
Highlights
Wearable sensor technology has steadily grown in availability within a wide variety of well-established consumer and medical devices
Wearable sensors are widely used in healthcare, due to their hardware capacity, small footprint and lower cost compared to equivalent medical instruments capable of monitoring the same vital signs [2]
Convolutional Neural Network (CNN) may perform better than Long-ShortTerm Memory (LSTM)-Recurrent Neural Network (RNN) for real-time datasets
Summary
Wearable sensor technology has steadily grown in availability within a wide variety of well-established consumer and medical devices. Data gloves are being tested at the research level for their effectiveness in capturing information on finger movements and tremors, as well as finger joint limitations. These gadgets are commercially available and are frequently utilised in fitness tracking and healthcare monitoring. Tmaebnlet t1opqruovanidtiefsyadsiusemasmeaprryoogfrevsasriioonu.s wearable technology devices are commonly used in a healthcare environment to quantify disease progression. AA ddaattaagglloovvee eeqquuipipppeeddwwitihthIMIMUUsseennssoorrssccaannddeetetecctthhaannddsstatabbiliiltiyty,,aannddaassaarreessuulltt,,jojoiinnttssttiiffffnneessssccaannbbee idideenntitfiifieeddwwitihthrreeaassoonnaabblelepprreeccisisioionn..MMaacchhinineeLLeeaarrnniningg((MMLL))aalglgoorritihthmmssbbaasseeddoonnlliinneeaarr rreeggrreessssiioonnccaannaalslsooddeetteerrmmiinneetthheeddisiseeaasseesseerrioiouussnneessssooffRRAAuussininggaassmmaarrtptphhoonnee,,rreedduucciinngg ththeenneeeeddttoovviissititaacclliinniicciiaann..IInnaassmmaarrttpphhoonneeaapppplilcicaatitoionn, ,tthheeddeevviciceewwililllttrraaiinnuussiinnggtthhee ppaatiteienntsts’’DDAASS2288aannddHHAAQQssccoorreess,,aasswweellllaassaasseellff--aasssseesssseeddtteennddeerrjojoiinnttccoouunntt((ssTTJJCC))aanndd aasseelflf--aasssseesssseeddsswwoolllelennjojoininttccoouunntt((ssSSJJCC))..PPaattiieennttss’’ttrruunnkkaacccceelleerraattiioonnccaappttuurreedd dduurriinngg wwaalklkininggccaannbbeemmeeaassuurreeddwwiitthhaassmmaarrttpphhoonneeaapppplliiccaattiioonn..TThhisispprreeddiiccttiivveemmooddeellaaccccoouunnttss foforr6677%%ooff tthhee DDAASS2288 vvaarriiaannccee. IIMMUU sseennssoorrss aarree aattttaacchheedd ttoo tthheeccoollllaarr, ,hheeaadd, ,aannddlulummbabrarspsipnienetotogagtahtehrerrarnagnegoef omf motoiotinon(R(OROMM) d) dataat.a.TThheefufullllRROOMMvvaalluueessccaallccuullaatteedd oovveerr aa sseerriieess ooff mmoottiioonnss iissuusseeddttoo ccaalclcuulalateteddisiseeaasseeinintetennssitiyty..TThhuuss, ,wweeaarraabblelesseennssoorrsshhaavveebbeeeennuusseeddttoossuuppppoorrttcclliinniicciiaannss dduurrininggththeeddiaiaggnnoossisisaannddrreehhaabbiilliittaattiioonnooffppaattiieennttssssuuffffeerriinnggffrroomm RRAA aanndd AASS. TThheeisissuueeooffuussininggvvaarrioiouusshheeaaltlthhaapppprraaisisaallqquueessttioionnnnaaiirreessiisstthhaatt((aa))iiffaannaalltteerrnnaattiivvee isisnnoottaavvaailialabbleletotopaptaietinetnst,st,htehyeycacnanlealevaeviet intunlluollroNr /NA/rAathrearthtehrantheannteernintegridnegtadilestaoinls wohnawt thhaetirthoepiirnoiopninaitonthat thimatet(ibm)eth(eb)ptahien pscaainlesccaanlenoctanbne outsbede utoseadcctuoratcecluyrmatealpypmaianp mpaginnimtuadgenaitnudde(ca) nadll a(cn)sawllerasndswepeerns ddeopneandpaotnienatp’sactiuernrte’nstcpuhrryesnictapl haynsdicmaleanntadl mcoenndtia-l ticoonnsdwitihoincshwcahnicbheciannflubeninceflduebnycoedthbeyr foatchtoersf,ascutocrhs,asupcrhesacsrpibredscdrirbuegdsdorruegms otrioenmaoltsitoantael [6st8a]t.e [68]
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