A Smart Medication Prompting System and Context Reasoning in Home Environments

Lim, Myungeun; Choi, Jae-Hun; Kim, Dae–Hee; Park, Soojun

doi:10.1109/ncm.2008.254

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…Many early systems were rule-driven or required knowledge of a user's daily schedule, employing dynamic Bayesian networks and similar techniques to produce prompts [3,7,8,9,10]. While these systems are able to adjust prompts based on user activities, they also required input of a user's daily schedule or predefined activity steps.…”

Section: Related Workmentioning

confidence: 99%

Regression tree classification for activity prediction in smart homes

Minor

Cook

2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

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The growing number of older adults in the population has created an increasing need for health-assistive systems, including prompting interventions to provide activity reminders. In this paper, we present a new regression-tree-based activity forecasting algorithm to predict the occurrence of future activities for prompting initiation of such activities. This automated algorithm extracts high-level features from sensor events and inputs these features to a machine learning algorithm which forecasts when a target activity will next occur. We compare this system to a standard linear regression classification using real data from smart homes. The forecasting algorithm is shown to provide lower error rates over the linear regression model.

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Section: Related Workmentioning

confidence: 99%

Regression tree classification for activity prediction in smart homes

Minor

Cook

2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

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show abstract

“…In this approach, a set of rules is defined based on time, the context of an activity and user preferences. Lim et al [Lim et al(2008) Lim, Choi, Kim, and Park] designed a medication reminder system that recognizes the service (composed of a digital health frame, medicine chest and medication prompting application) suitable for a medication situation. Oriani et al [Oriani et al(2003) Oriani, Moniz-Cook, Binetti, Zanieri, Frisoni, Geroldi, De Vreese, and Zanetti] developed an electronic memory aid that allows a user or caregiver to prerecord messages (e.g.…”

Section: Related Workmentioning

confidence: 99%

PUCK: an automated prompting system for smart environments: toward achieving automated prompting—challenges involved

Das

Cook

Schmitter‐Edgecombe

et al. 2011

Pers Ubiquit Comput

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The growth in popularity of smart environments has been quite steep in the last decade and so has the demand for smart health assistance systems. A smart home-based prompting system can enhance these technologies to deliver in-home interventions to users for timely reminders or brief instructions describing the way a task should be done for successful completion. This technology is in high demand given the desire of people who have physical or cognitive limitations to live independently in their homes. In this paper, with the introduction of the “PUCK” prompting system, we take an approach to automate prompting-based interventions without any predefined rule sets or user feedback. Unlike other approaches, we use simple off-the-shelf sensors and learn the timing for prompts based on real data that is collected with volunteer participants in our smart home test bed. The data mining approaches taken to solve this problem come with the challenge of an imbalanced class distribution that occurs naturally in the data. We propose a variant of an existing sampling technique, SMOTE, to deal with the class imbalance problem. To validate the approach, a comparative analysis with Cost Sensitive Learning is performed.

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“…In this approach, a set of rules is defined based on time, the context of an activity and user preferences. Lim et al [2] designed a medication reminder system that recognizes the reminders suitable for medication situation. Rudary et al [3] integrated temporal constraint reasoning with reinforcement learning to build an adaptive reminder system.…”

Section: Related Workmentioning

confidence: 99%

An Automated Prompting System for Smart Environments

Das

Chen

Seelye

et al. 2011

Toward Useful Services for Elderly and People With Disabilities

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Abstract. The growth in popularity of smart environments has been quite steep in the last decade and so has the demand for smart health assistance systems. A smart home-based prompting system can enhance these technologies to deliver in-home interventions to a user for timely reminders or a brief instruction describing the way a task should be done for successful completion. This technology is in high demand with the desire for people who have physical or cognitive limitations to live independently in their homes. In this paper, we take the approach to fully automating a prompting system without any predefined rule set or user feedback. Unlike other approaches, we use simple off-the-shelf sensors and learn the timing for prompts based on real data that is collected with volunteer participants in our smart home testbed.

show abstract

A Smart Medication Prompting System and Context Reasoning in Home Environments

Cited by 12 publications

References 5 publications

Regression tree classification for activity prediction in smart homes

Regression tree classification for activity prediction in smart homes

PUCK: an automated prompting system for smart environments: toward achieving automated prompting—challenges involved

An Automated Prompting System for Smart Environments

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