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Jae Ma
Department of Interior Architecture Design, Hanyang University, 04763 Seoul, Korea

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Journal article
Published: 11 February 2021 in Sensors
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An integrated smart home system (ISHS) is an effective way to improve the quality of life of the elderly. The elderly’s willingness is essential to adopt an ISHS; to the best of our knowledge, no study has investigated the elderly’s perception of ISHS. Consequently, this study aims to investigate the elderly’s perception of the ISHS by comprehensively evaluating its possible benefits and negative responses. A set of sensors required for an ISHS was determined, and interviews were designed based on four factors: perceived comfort, perceived usability, perceived privacy, and perceived benefit. Subsequently, technological trials of the sensor-set followed by two focus group interviews were conducted on nine independently living elderly participants at a senior welfare center in South Korea. Consistent with previous studies, the results of this investigation indicate that elderly participants elicited negative responses regarding usability complexity, and discomfort to daily activities. Despite such negative responses, after acquiring enough awareness about the ISHS’s benefits, the elderly acknowledged its necessity and showed a high level of willingness. Furthermore, these results indicate that for a better adoption of an ISHS, sufficient awareness regarding its benefits and development of elderly-friendly smart home sensors that minimize negative responses are required.

ACS Style

Tae Jo; Jae Ma; Seung Cha. Elderly Perception on the Internet of Things-Based Integrated Smart-Home System. Sensors 2021, 21, 1284 .

AMA Style

Tae Jo, Jae Ma, Seung Cha. Elderly Perception on the Internet of Things-Based Integrated Smart-Home System. Sensors. 2021; 21 (4):1284.

Chicago/Turabian Style

Tae Jo; Jae Ma; Seung Cha. 2021. "Elderly Perception on the Internet of Things-Based Integrated Smart-Home System." Sensors 21, no. 4: 1284.

Journal article
Published: 14 December 2020 in Building and Environment
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To plan successful activity-based workplaces (ABW), architects need to clearly understand user-specific activity patterns through the accurate recognition of user activity. Because user activity is closely associated with space, equipment, and users, such diverse activity-related information should be essentially considered for accurate activity recognition. However, previous activity recognition methods have limitations for accurately recognizing user activity for ABW planning, because they only relied on sensor-estimated data and are, therefore, unable to comprehensively consider diverse activity-related information. The study thus integrates site investigation and sensor estimation using a Bluetooth Low Energy beacon and accelerometer for accurately recognizing user activity based on diverse activity-related information. We defined five important items of activity-related information (user actions, number of nearby users, function of space, equipment located in space, and space use policy) and developed a user-specific activity pattern generation (UAPG) framework consisting of three stages: (1) the preparation stage, (2) sensor-estimation stage, and (3) activity pattern generation stage. The demonstration was conducted through scenario-based experiments in an academic office building. In the demonstration, the UAPG framework achieved 91.8% of activity recognition accuracy and successfully generated user-specific activity patterns. In addition, information regarding space usage, proportion of activities, and spatial preference of the user was generated based on a user-specific activity pattern. Such objective information provided by the UAPG framework enables evidence-based ABW planning that efficiently accommodates users with minimal office space, while simultaneously increasing their satisfaction and productivity.

ACS Style

Jae Hoon Ma; Seung Hyun Cha. A user-specific activity pattern generation framework for evidence-based ABW planning. Building and Environment 2020, 189, 107519 .

AMA Style

Jae Hoon Ma, Seung Hyun Cha. A user-specific activity pattern generation framework for evidence-based ABW planning. Building and Environment. 2020; 189 ():107519.

Chicago/Turabian Style

Jae Hoon Ma; Seung Hyun Cha. 2020. "A user-specific activity pattern generation framework for evidence-based ABW planning." Building and Environment 189, no. : 107519.

Journal article
Published: 22 July 2020 in Automation in Construction
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Accurate face-to-face interaction estimation is required for a successful data-driven design in workplaces. In previous studies, various sensor-based interaction estimation methods which use proximity and speaking data have been developed. However, these data alone cannot confirm the presence of interactions because non-interacting users also engage in speaking activities. This study aims to develop a novel turn-taking pattern-based interaction estimation (i.e., TIE) framework that integrates turn-taking with location data. The framework estimates interactions in three steps: 1) co-location estimation using a Bluetooth Low Energy beacon; 2) speaking-turn ascertainment through volume-based speaker identification; and 3) interaction group recognition based on turn-taking pattern analysis. Using three different experimental scenarios, the interaction estimation accuracy of the framework was demonstrated to be 77.7%. In the absence of co-location estimation errors, the interaction estimation accuracy increases to 95.5%. The demonstration results indicate that the TIE framework has potential for accurate interaction estimation in workplaces.

ACS Style

Jae Hoon Ma; Seung Hyun Cha. A human data-driven interaction estimation using IoT sensors for workplace design. Automation in Construction 2020, 119, 103352 .

AMA Style

Jae Hoon Ma, Seung Hyun Cha. A human data-driven interaction estimation using IoT sensors for workplace design. Automation in Construction. 2020; 119 ():103352.

Chicago/Turabian Style

Jae Hoon Ma; Seung Hyun Cha. 2020. "A human data-driven interaction estimation using IoT sensors for workplace design." Automation in Construction 119, no. : 103352.