{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:23:25Z","timestamp":1785918205287,"version":"3.56.0"},"reference-count":73,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,11]],"date-time":"2017-04-11T00:00:00Z","timestamp":1491868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have been demonstrated to be useful in the monitoring of motor symptoms of Parkinson\u2019s disease (PD). In this sense, most of previous works have attempted to assess PD symptoms in controlled environments or short tests. This paper presents the design of an IMU, called 9 \u00d7 3, that aims to assess PD symptoms, enabling the possibility to perform a map of patients\u2019 symptoms at their homes during long periods. The device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9 \u00d7 3, storing inertial information and algorithm outputs, sending messages to external devices and being able to detect freezing of gait and bradykinetic gait. Results obtained in 12 patients exhibit a sensitivity and specificity over 80%. Additionally, the system enables working 23 days (at waking hours) with a 1200 mAh battery and a sampling rate of 50 Hz, opening up the possibility to be used for other applications like wellbeing and sports.<\/jats:p>","DOI":"10.3390\/s17040827","type":"journal-article","created":{"date-parts":[[2017,4,11]],"date-time":"2017-04-11T11:41:42Z","timestamp":1491910902000},"page":"827","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson\u2019s Disease Patients"],"prefix":"10.3390","volume":"17","author":[{"given":"Daniel","family":"Rodr\u00edguez-Mart\u00edn","sequence":"first","affiliation":[{"name":"Technical Research Centre for Dependency Care and Autonomous Living\u2014CETPD, Universitat Polit\u00e8cnica de Catalunya\u2014BarcelonaTech, Rambla de l\u2019Exposici\u00f3 59-69, Vilanova i la Geltr\u00fa, 08800 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"P\u00e9rez-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Technical Research Centre for Dependency Care and Autonomous Living\u2014CETPD, Universitat Polit\u00e8cnica de Catalunya\u2014BarcelonaTech, Rambla de l\u2019Exposici\u00f3 59-69, Vilanova i la Geltr\u00fa, 08800 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert","family":"Sam\u00e0","sequence":"additional","affiliation":[{"name":"Technical Research Centre for Dependency Care and Autonomous Living\u2014CETPD, Universitat Polit\u00e8cnica de Catalunya\u2014BarcelonaTech, Rambla de l\u2019Exposici\u00f3 59-69, Vilanova i la Geltr\u00fa, 08800 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreu","family":"Catal\u00e0","sequence":"additional","affiliation":[{"name":"Technical Research Centre for Dependency Care and Autonomous Living\u2014CETPD, Universitat Polit\u00e8cnica de Catalunya\u2014BarcelonaTech, Rambla de l\u2019Exposici\u00f3 59-69, Vilanova i la Geltr\u00fa, 08800 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joan","family":"Moreno Arostegui","sequence":"additional","affiliation":[{"name":"Technical Research Centre for Dependency Care and Autonomous Living\u2014CETPD, Universitat Polit\u00e8cnica de Catalunya\u2014BarcelonaTech, Rambla de l\u2019Exposici\u00f3 59-69, Vilanova i la Geltr\u00fa, 08800 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joan","family":"Cabestany","sequence":"additional","affiliation":[{"name":"Technical Research Centre for Dependency Care and Autonomous Living\u2014CETPD, Universitat Polit\u00e8cnica de Catalunya\u2014BarcelonaTech, Rambla de l\u2019Exposici\u00f3 59-69, Vilanova i la Geltr\u00fa, 08800 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Berta","family":"Mestre","sequence":"additional","affiliation":[{"name":"Unidad de Parkinson y Trastornos del Movimiento (UParkinson), Passeig Bonanova 26, 08022 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheila","family":"Alcaine","sequence":"additional","affiliation":[{"name":"Unidad de Parkinson y Trastornos del Movimiento (UParkinson), Passeig Bonanova 26, 08022 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Prats","sequence":"additional","affiliation":[{"name":"Unidad de Parkinson y Trastornos del Movimiento (UParkinson), Passeig Bonanova 26, 08022 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda","family":"Cruz Crespo","sequence":"additional","affiliation":[{"name":"Unidad de Parkinson y Trastornos del Movimiento (UParkinson), Passeig Bonanova 26, 08022 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c0ngels","family":"Bay\u00e9s","sequence":"additional","affiliation":[{"name":"Unidad de Parkinson y Trastornos del Movimiento (UParkinson), Passeig Bonanova 26, 08022 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10691","DOI":"10.3390\/s140610691","article-title":"Detecting falls with wearable sensors using machine learning techniques","volume":"14","author":"Ozdemir","year":"2014","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.gaitpost.2006.09.012","article-title":"Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm","volume":"26","author":"Bourke","year":"2007","journal-title":"Gait 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