Health score prediction using low-invasive sensors

Shimosaka, Masamichi; Masuda, Shinya; Takeichi, Kazunari; Fukui, Rui; Sato, Tomomasa

doi:10.1145/2370216.2370439

Cited by 6 publications

(2 citation statements)

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“…residents either have a coffee machine or a kettle). As it has been reported that ADL scores can be predicted from the long-term location and movement records obtained from solitary elderly people [29], in addition to these being common to all apartments, we chose to use only the motion sensors data.…”

Section: Field Trialmentioning

confidence: 99%

Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes

2023

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This work presents a novel method for motion sensor placement within smart homes. Using recordings from 3D depth cameras within six real homes, clusters are created with the resident's tracked location. The resulting clusters identify the possible position of a sensor and its field of view. By using a sequence of clusters as input to a Recurrent Neural Network, we evaluate our method on the task of activity recognition and prediction. These results are compared to using sensor events as input sequence, from motion sensors that were installed empirically in the same homes. Different clustering methods are investigated and all outperform the installed motion sensors, achieving a significant increase of prediction accuracy and F1-score.

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Section: Field Trialmentioning

confidence: 99%

Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes

2023

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“…activity [82] blood [93] phys. activity [168] context [27] hand [70] mental health [176] alcohol [107] eye contact [245] health [193] cocaine [155] frustration [217] eating [15,39,146,147,219] car danger [129] cognition [188] year cognitive complexity Figure 3.1: The domains of mobile sensing papers published at UbiComp 1999-2018. The cognitive complexity is a subjective assessment; overlaps have been handled by slightly moving domains with similar complexities.…”

Section: Ubiquitous Computing and Mobile Sensingmentioning

confidence: 99%

Computer-Cognition Interfaces: Sensing and influencing mental processes with computer interaction

Mottelson¹

2019

Preprint

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The variety of information about users hidden in the details of interaction data is increasingly being utilized for recognizing complex mental processes. Digital systems can correspondingly influence mental processes of users, paving the way for new interactive systems that interface with the human mind. This thesis presents advances to such interfaces: through four papers I show how human affect and cognition can be sensed and influenced computationally.Paper 1 presents two studies that together show that affect influences mobile interaction, which allows for binary discrimination between neutral and positive affect using sensor led machine learning classification. Paper 2 builds upon the methods presented in Paper 1 and extends the classification domain to dishonesty, also using mobile interaction data. The paper shows across three studies how dishonesty and honesty vary in interactional details, and how this difference can be utilized for estimating the veracity of user behavior based on features that are engineered by mobile interaction data.Paper 3 presents a feasibility study of conducting virtual reality studies outside a laboratory, to increase heterogeneity and power. The paper shows through two studies how a range of VR tasks can be conducted without the use of an immediate experimenter, with participants carrying out experiments themselves. In Paper 4 I apply this methodology, and conduct a VR study with more than 200 participants to study how manipulations to avatars can influence affect responses. The paper presents evidence supporting the link between affect and avatars, and additionally discusses the interplay between positive affect and body ownership.

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