Home Activity Monitoring Research Articles

Leveraging mist and fog for big data analytics in IoT environment

AbstractInternet of Things (IoT) emerged as one of the leading technological advancements of our days. IoT generates enormous quantities of valuable data that need on time processing, resulting in reliable, and accurate decisions based on the Internet of Things vision. The quality of the generated data is inadequate, incomplete, uncertain, and produced from multiple sources. Although cloud servers can analyze and store enormous data, they need a lot of time to send full‐size data for storage and analysis as well as the high overhead they have that not satisfactory in many applications. This article provides a systematic way to review the IoT environment according to big data analytics together with limitations and challenges. Moreover, a cloud‐fog‐mist combination for handling IoT data concerning centralized and distributed data mining is explained. A proposed hybrid real‐time remote patient monitoring framework introduced that consists of the integration among the mist, fog, and cloud for healthcare treatment, which remote‐monitors patients continuously. In addition, Reduced‐Error Pruning tree (REPtree), MultiLayer Perceptron, naïve Bayes, and Sequential Minimal Optimization algorithms have applied to “Gas sensors for home activity monitoring” dataset to demonstrate the feasibility of traditional data mining algorithms to IoT data. The results showed that the REPtree algorithm achieved better accuracy against others with accuracy ranged between 90.66% and 93.6% according to the size of the data used in the study. Still, for the time metric, naive Bayes outperformed them with the lowest time between 1 and 18 seconds for building the model.

P.429 - Innovative home activity monitoring in non-ambulant patients with spinal muscular atrophy: a multicenter observational trial

P.429 - Innovative home activity monitoring in non-ambulant patients with spinal muscular atrophy: a multicenter observational trial

Regularity and Predictability of Human Mobility in Personal Space

Fundamental laws governing human mobility have many important applications such as forecasting and controlling epidemics or optimizing transportation systems. These mobility patterns, studied in the context of out of home activity during travel or social interactions with observations recorded from cell phone use or diffusion of money, suggest that in extra-personal space humans follow a high degree of temporal and spatial regularity – most often in the form of time-independent universal scaling laws. Here we show that mobility patterns of older individuals in their home also show a high degree of predictability and regularity, although in a different way than has been reported for out-of-home mobility. Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity) is uncovered when explicitly accounting for contextual data in a model of in-home mobility. These results suggest that human mobility in personal space is highly stereotyped, and that monitoring discontinuities in routine room-level mobility patterns may provide an opportunity to predict individual human health and functional status or detect adverse events and trends.

Sensor-based home activity monitoring of geriatric fracture patients in rehabilitation – The GAL NATARS study

O 013 Sensor-based home activity monitoring of geriatric fracture patients in rehabilitation –The GAL NATARS study M. Marscholleka,b,c,d,e,f,g,h,i, M. Apela,b,c,d,e,f,g,h,i, P. Bentea,b,c,d,e,f,g,h,i, L. Dasenbrocka,b,c,d,e,f,g,h,i, K. Elbersa,b,c,d,e,f,g,h,i, R. Hauxa,b,c,d,e,f,g,h,i, A. Heina,b,c,d,e,f,g,h,i, G. Kolba,b,c,d,e,f,g,h,i, H. Kunemunda,b,c,d,e,f,g,h,i, C. Lammel-Polchaua,b,c,d,e,f,g,h,i, M. Meisa,b,c,d,e,f,g,h,i, H. Meyer zu Schwabedissena,b,c,d,e,f,g,h,i, H. Remmersa,b,c,d,e,f,g,h,i, E.E. Steena,b,c,d,e,f,g,h,i, J. Wanga,b,c,d,e,f,g,h,i, K.H. Wolf a,b,c,d,e,f,g,h,i, J. Bauera,b,c,d,e,f,g,h,i a Hannover Medical School, Hannover, Germany b Klinikum Oldenburg, Oldenburg. Germany c Std. Klinikum Braunschweig, Braunschweig, Germany d St. Bonifatius Hospital Lingen, Lingen, Germany e TU Braunschweig, Braunschweig, Germany f OFFIS, Oldenburg, Germany g Universitat Vechta, Vechta, Germany h Horzentrum Oldenburg, Oldenburg, Germany i Universitat Osnabruck, Osnabruck, Germany

P43 Assessing the validity of a home activity monitor in patients with COPD

BackgroundSelf-reported physical activity equivalent to at least 2 h a week is associated with reduction in the risk of hospital admission and death in COPD.1 However, whether self reported activity...

P42 The relationship of home activity levels to psychological co-morbidity in COPD

BackgroundPhysical activity levels are often reduced in patients with COPD, but are partially amenable to intervention, predominantly by rehabilitation. Depression and anxiety are also commonly observed co-morbidities, which relate to...

191 ‘They said it couldn’t be done!’ - Home Activity Monitoring Project (HAMP) – an evaluation of a remote controlled, home-based physical activity program

191 ‘They said it couldn’t be done!’ - Home Activity Monitoring Project (HAMP) – an evaluation of a remote controlled, home-based physical activity program