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      Indirect Recognition of Predefined Human Activities

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          Abstract

          The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO 2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.

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          A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study.

          J R Le Gall, S Lemeshow, F Saulnier (1993)
          To develop and validate a new Simplified Acute Physiology Score, the SAPS II, from a large sample of surgical and medical patients, and to provide a method to convert the score to a probability of hospital mortality. The SAPS II and the probability of hospital mortality were developed and validated using data from consecutive admissions to 137 adult medical and/or surgical intensive care units in 12 countries. The 13,152 patients were randomly divided into developmental (65%) and validation (35%) samples. Patients younger than 18 years, burn patients, coronary care patients, and cardiac surgery patients were excluded. Vital status at hospital discharge. The SAPS II includes only 17 variables: 12 physiology variables, age, type of admission (scheduled surgical, unscheduled surgical, or medical), and three underlying disease variables (acquired immunodeficiency syndrome, metastatic cancer, and hematologic malignancy). Goodness-of-fit tests indicated that the model performed well in the developmental sample and validated well in an independent sample of patients (P = .883 and P = .104 in the developmental and validation samples, respectively). The area under the receiver operating characteristic curve was 0.88 in the developmental sample and 0.86 in the validation sample. The SAPS II, based on a large international sample of patients, provides an estimate of the risk of death without having to specify a primary diagnosis. This is a starting point for future evaluation of the efficiency of intensive care units.
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            A Survey on Human Activity Recognition using Wearable Sensors

            Oscar Lara, Miguel Labrador (2014)
            Article 0 comments Cited 382 times – based on 0 reviews     Review now
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              Activity recognition using cell phone accelerometers

              Jennifer Kwapisz, Gary M. Weiss, Samuel Moore (2011)
              Article 0 comments Cited 324 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Journal ID (publisher-id): sensors
                Title: Sensors (Basel, Switzerland)
                Publisher: MDPI
                ISSN (Electronic): 1424-8220
                Publication date (Electronic): 26 August 2020
                Publication date Collection: September 2020
                Volume: 20
                Issue: 17
                Electronic Location Identifier: 4829
                Affiliations
                Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 70833 Ostrava-Poruba, Czech Republic; antonino.proto@ 123456vsb.cz (A.P.); jan.vanus@ 123456vsb.cz (J.V.); petr.bilik@ 123456vsb.cz (P.B.)
                Author notes
                Author information
                https://orcid.org/0000-0002-8657-275X
                https://orcid.org/0000-0003-1964-8184
                https://orcid.org/0000-0003-3802-0595
                https://orcid.org/0000-0001-8655-778X
                Article
                Publisher ID: sensors-20-04829
                DOI: 10.3390/s20174829
                PMC ID: 7506661
                SO-VID: 3282f7b0-5389-4f7f-add0-8ee91cc158d9
                Copyright © © 2020 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 31 July 2020
                Date accepted : 25 August 2020
                Categories
                Subject: Article

                ScienceOpen disciplines: Biomedical engineering
                Keywords: deep learning,logistic regression,activity recognition,prediction,classification,artificial neural network,smart homes,intelligent buildings
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: deep learning, logistic regression, activity recognition, prediction, classification, artificial neural network, smart homes, intelligent buildings

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