The Experts below are selected from a list of 75 Experts worldwide ranked by ideXlab platform
Masahide Nakamura - One of the best experts on this subject based on the ideXlab platform.
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Using Non-Intrusive Environmental Sensing for ADLS Recognition in One-Person Household
International Journal of Software Innovation, 2018Co-Authors: Long Niu, Sachio Saiki, Masahide NakamuraAbstract:This article describes how pervasive sensing technologies are promising for increasing One-Person Household (OPH), where a system monitors and assists a resident to maintain healthy life rhythm. Automatic recognition of activities of daily living (ADLS) has been a hot research topic in pervasive computing. However, most existing methods have limitations in development cost, privacy exposure, and inconvenience for residents. To cope with the limitations, this article presents a new ADL recognition system especially for OPH. To minimize the development cost as well as intrusions to user and house, the system exploits an IoT-based environment sensing device, called autonomous sensor box (sensorbox) which can autonomously measure 7 kinds of environment attributes. The system then applies machine-learning techniques to predict 7 kinds of ADLS. Finally, this article conducts an experiment within an actual apartment of a single user. The result shows that the proposed system achieves the average accuracy of ADLS recognition with about 88%, by carefully developing the features of environment attributes.
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SNPD - Recognizing ADLs of one person Household based on non-intrusive environmental sensing
2017 18th IEEE ACIS International Conference on Software Engineering Artificial Intelligence Networking and Parallel Distributed Computing (SNPD), 2017Co-Authors: Long Niu, Sachio Saiki, Masahide NakamuraAbstract:Pervasive sensing technologies are promising for increasing One-Person Households (OPH), where the sensors monitor and assist the resident to maintain healthy life rhythm. Towards the practical use, the recognition of activities of daily living (ADL) is an important step. Many studies of the ADL recognition have been conducted so far, for real-life and human-centric applications such as eldercare and healthcare. However, most existing methods have limitations in deployment cost, privacy exposure, and inconvenience for residents. To cope with the limitations, this paper presents a new indoor ADL recognition system especially for OPH. To minimize the deployment cost as well as the intrusions to user and house, we exploit an IoT-based environment-sensing device, called Autonomous Sensor Box (SensorBox) which can autonomously measure 7 kinds of environment attributes. We apply machine-learning techniques to the collected data, and predicts 7 kinds of ADLs. We conduct an experiment within an actual apartment of a single user. The result shows that the proposed system achieves the average accuracy of ADL recognition with more than 88%, by carefully developing the features of environment attributes.
Long Niu - One of the best experts on this subject based on the ideXlab platform.
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Using Non-Intrusive Environmental Sensing for ADLS Recognition in One-Person Household
International Journal of Software Innovation, 2018Co-Authors: Long Niu, Sachio Saiki, Masahide NakamuraAbstract:This article describes how pervasive sensing technologies are promising for increasing One-Person Household (OPH), where a system monitors and assists a resident to maintain healthy life rhythm. Automatic recognition of activities of daily living (ADLS) has been a hot research topic in pervasive computing. However, most existing methods have limitations in development cost, privacy exposure, and inconvenience for residents. To cope with the limitations, this article presents a new ADL recognition system especially for OPH. To minimize the development cost as well as intrusions to user and house, the system exploits an IoT-based environment sensing device, called autonomous sensor box (sensorbox) which can autonomously measure 7 kinds of environment attributes. The system then applies machine-learning techniques to predict 7 kinds of ADLS. Finally, this article conducts an experiment within an actual apartment of a single user. The result shows that the proposed system achieves the average accuracy of ADLS recognition with about 88%, by carefully developing the features of environment attributes.
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SNPD - Recognizing ADLs of one person Household based on non-intrusive environmental sensing
2017 18th IEEE ACIS International Conference on Software Engineering Artificial Intelligence Networking and Parallel Distributed Computing (SNPD), 2017Co-Authors: Long Niu, Sachio Saiki, Masahide NakamuraAbstract:Pervasive sensing technologies are promising for increasing One-Person Households (OPH), where the sensors monitor and assist the resident to maintain healthy life rhythm. Towards the practical use, the recognition of activities of daily living (ADL) is an important step. Many studies of the ADL recognition have been conducted so far, for real-life and human-centric applications such as eldercare and healthcare. However, most existing methods have limitations in deployment cost, privacy exposure, and inconvenience for residents. To cope with the limitations, this paper presents a new indoor ADL recognition system especially for OPH. To minimize the deployment cost as well as the intrusions to user and house, we exploit an IoT-based environment-sensing device, called Autonomous Sensor Box (SensorBox) which can autonomously measure 7 kinds of environment attributes. We apply machine-learning techniques to the collected data, and predicts 7 kinds of ADLs. We conduct an experiment within an actual apartment of a single user. The result shows that the proposed system achieves the average accuracy of ADL recognition with more than 88%, by carefully developing the features of environment attributes.
Maryann Wulff - One of the best experts on this subject based on the ideXlab platform.
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Growth and change in one person Households: Implications for the housing market
Urban Policy and Research, 2001Co-Authors: Maryann WulffAbstract:Abstract As a result of several demographic and social trends, the one person Household accounts for approximately one quarter of all Households in Australia and is expected to further expand to one‐third by 2021. After couple families, the single person living alone is the most numerous Household form. The paper differentiates one person Households by life course stage and considers their likely housing demand in terms of tenure, dwelling type and number of bedrooms. Original analysis of the 1996 ABS 1% Household Sample File has been undertaken. Home ownership increased over the life course for singles, but not to the same extent as for other Households. Moreover, there is some evidence to suggest that, Household income aside, people living alone may prefer flats, units or apartments over detached houses. The paper suggests that our understanding of housing careers needs to incorporate the possibility of one or more spells as a one person Household.
John C Anyanwu - One of the best experts on this subject based on the ideXlab platform.
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marital status Household size and poverty in nigeria evidence from the 2009 2010 survey data
African Development Review, 2014Co-Authors: John C AnyanwuAbstract:Abstract This paper examines the effect of marital status and Household size, among other correlates, on poverty in Nigeria, using the Harmonized Nigeria Living Standard Survey (HNLSS) data of 2009/2010. Our results show that monogamous marriage, divorce/separation and widowhood are negatively and significantly correlated with the probability of being poor. However, monogamous marriage has the largest probability of reducing poverty in Nigeria. We also find that Household size matters in determining poverty in the country: a One-Person Household negatively and significantly reduces poverty while addition of members to the Household, progressively increases the probability of being poor. In addition, our results show that there is a significant concave (inverted-U shaped) relationship between age and poverty. Other variables found to significantly reduce the probability of being poor include: being a male, completion of post-secondary education, being in paid Household employment, and residence in the North Central and South East geopolitical zones. Variables that increase the probability of being poor in Nigeria include rural residence, possessing no education, being a self-employed farmer, and residence in the North West geopolitical zone of the country. Based on the results, we recommend a number of policy interventions necessary to reduce poverty in Nigeria. Keywords: Marital Status, Household Size, Poverty, Nigeria JEL Classification: I32, I38, J11, J12
Sachio Saiki - One of the best experts on this subject based on the ideXlab platform.
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Using Non-Intrusive Environmental Sensing for ADLS Recognition in One-Person Household
International Journal of Software Innovation, 2018Co-Authors: Long Niu, Sachio Saiki, Masahide NakamuraAbstract:This article describes how pervasive sensing technologies are promising for increasing One-Person Household (OPH), where a system monitors and assists a resident to maintain healthy life rhythm. Automatic recognition of activities of daily living (ADLS) has been a hot research topic in pervasive computing. However, most existing methods have limitations in development cost, privacy exposure, and inconvenience for residents. To cope with the limitations, this article presents a new ADL recognition system especially for OPH. To minimize the development cost as well as intrusions to user and house, the system exploits an IoT-based environment sensing device, called autonomous sensor box (sensorbox) which can autonomously measure 7 kinds of environment attributes. The system then applies machine-learning techniques to predict 7 kinds of ADLS. Finally, this article conducts an experiment within an actual apartment of a single user. The result shows that the proposed system achieves the average accuracy of ADLS recognition with about 88%, by carefully developing the features of environment attributes.
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SNPD - Recognizing ADLs of one person Household based on non-intrusive environmental sensing
2017 18th IEEE ACIS International Conference on Software Engineering Artificial Intelligence Networking and Parallel Distributed Computing (SNPD), 2017Co-Authors: Long Niu, Sachio Saiki, Masahide NakamuraAbstract:Pervasive sensing technologies are promising for increasing One-Person Households (OPH), where the sensors monitor and assist the resident to maintain healthy life rhythm. Towards the practical use, the recognition of activities of daily living (ADL) is an important step. Many studies of the ADL recognition have been conducted so far, for real-life and human-centric applications such as eldercare and healthcare. However, most existing methods have limitations in deployment cost, privacy exposure, and inconvenience for residents. To cope with the limitations, this paper presents a new indoor ADL recognition system especially for OPH. To minimize the deployment cost as well as the intrusions to user and house, we exploit an IoT-based environment-sensing device, called Autonomous Sensor Box (SensorBox) which can autonomously measure 7 kinds of environment attributes. We apply machine-learning techniques to the collected data, and predicts 7 kinds of ADLs. We conduct an experiment within an actual apartment of a single user. The result shows that the proposed system achieves the average accuracy of ADL recognition with more than 88%, by carefully developing the features of environment attributes.
