國立虎尾科技大學 |

Machine learning challenges for automated prompting in smart homes.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine learning challenges for automated prompting in smart homes./
作者:	Das, Barnan.
面頁冊數:	1 online resource (236 pages)
附註:	Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781321251586

Machine learning challenges for automated prompting in smart homes.
Das, Barnan.

Machine learning challenges for automated prompting in smart homes. - 1 online resource (236 pages)

Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.

Thesis (Ph.D.)--Washington State University, 2014.

Includes bibliographical references

As the world's population ages, there is an increased prevalence of diseases related to aging, such as dementia. Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions or prompts. This dissertation focuses on addressing machine learning challenges that arise while devising an effective automated prompting system.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781321251586Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Machine learning challenges for automated prompting in smart homes.
LDR:03633ntm a2200349K 4500 001 915393
005 20180727125214.5
006 m o u
007 cr mn||||a|a||
008 190606s2014 xx obm 000 0 eng d
020 $a 9781321251586
035 $a (MiAaPQ)AAI3640007
035 $a (MiAaPQ)wsu:11124
035 $a AAI3640007
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Das, Barnan. $3 1188728
245 1 0 $a Machine learning challenges for automated prompting in smart homes.
264 0 $c 2014
300 $a 1 online resource (236 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
500 $a Adviser: Diane J. Cook.
502 $a Thesis (Ph.D.)--Washington State University, 2014.
504 $a Includes bibliographical references
520 $a As the world's population ages, there is an increased prevalence of diseases related to aging, such as dementia. Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions or prompts. This dissertation focuses on addressing machine learning challenges that arise while devising an effective automated prompting system.
520 $a Our first goal is to emulate natural interventions provided by a caregiver to individuals with memory impairments, by using a supervised machine learning approach to classify pre-segmented activity steps into prompt or no-prompt classes. However, the lack of training examples representing prompt situations causes imbalanced class distribution. We proposed two probabilistic oversampling techniques, RACOG and wRACOG, that help in better learning of the "prompt" class. Moreover, there are certain prompt situations where the sensor triggering signature is quite similar to the situations when the participant would probably need no prompt. The absence of sufficient data attributes to differentiate between prompt and no-prompt classes causes class overlap. We propose ClusBUS, a clustering-based under-sampling technique that identifies ambiguous data regions. ClusBUS preprocesses the data in order to give more importance to the minority class during classification.
520 $a Our second goal is to automatically detect activity errors in real time, while an individual performs an activity. We propose a collection of one-class classification-based algorithms, known as DERT, that learns only from the normal activity patterns and without using any training examples for the activity errors. When evaluated on unseen activity data, DERT is able to identify abnormalities or errors, which can be potential prompt situations. We validate the effectiveness of the proposed algorithms in predicting potential prompt situations on the sensor data of ten activities of daily living, collected from 580 participants, who were part of two smart home studies.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Washington State University. $b Computer Science. $3 1186533
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3640007 $z click for full text (PQDT)