Embedded Sensor Systems for Health in Home Healthcare

Lindén, María; Björkman, Mats; Duboy, Miguel Ángel Valero

doi:10.4018/978-1-4666-9978-6.ch044

Cited by 3 publications

(4 citation statements)

References 29 publications

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“…Corruption of the activity data stream when activity data is transmitted via a low-power Bluetooth (BT) signal to the integrated system may affect the ability to use the information safely to improve insulin delivery. 53,54 The signal may also be interfered with by other BTLE devices or malicious interference. Currently as CLC systems are being developed, there is a need to share data within and between sub components of the CLC system and the clinical team.…”

Section: General Considerations Important In Translating Physical Actmentioning

confidence: 99%

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Physical Activity Capture Technology With Potential for Incorporation Into Closed-Loop Control for Type 1 Diabetes

Dadlani

Levine

McCrady-Spitzer

et al. 2015

J Diabetes Sci Technol

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Type 1 diabetes (T1D) is characterized by complete or near complete absence of endogenous insulin secretion resulting in the need to deliver exogenous insulin in an individualized, precise manner.1 Current clinically approved technologies to replace insulin result in unacceptable risk for hypoglycemia, hyperglycemia, and glycemic variability (GV).2 Changes in plasma glucose concentration are more pronounced in T1D, occur rapidly and are related to food intake, physical activity and imprecise insulin delivery. The maturation of technologies such as continuous glucose monitoring (CGM), continuous subcutaneous insulin infusion (CSII) or insulin pump, rapid acting insulin analogs and closed-loop control (CLC) algorithms that deliver insulin based on CGM data processing has resulted in CLC therapy becoming an area of active research with rapid therapeutic translation to clinical practice. 4 Current CLC studies show significant improvement in hypoglycemia and a bigger burden of hyperglycemia relative to time spent in hypoglycemic range. 4,5 Occurrence of hyper and hypoglycemia with current CLC, the burden associated with current CLC and limitations of eligible populations to use current technologies indicate the need for further innovation. Important variables in this context with potentially significant effect sizes are physical activity and meals. Significant efforts have been made to model the aforementioned parameters and understand their effect on glucose-insulin dynamics by incorporating/adapting them into CLC simulators. 6,7 Even though the day to day effect of physical activity on glucose status poses major challenges for T1D, limited research in this area has been conducted. 8 Since obesity and type 2 diabetes mellitus (T2D), 2 major disorders of whole body energy balance, continue to rise worldwide, most research to-date regarding physical activity capture device use has been pursued in these 2 disorders.9,10 T2D is characterized by hyperglycemia for a significant proportion of the day with the majority of such subjects having limited hypoglycemia AbstractPhysical activity is an important determinant of glucose variability in type 1 diabetes (T1D). It has been incorporated as a nonglucose input into closed-loop control (CLC) protocols for T1D during the last 4 years mainly by 3 research groups in single center based controlled clinical trials involving a maximum of 18 subjects in any 1 study. Although physical activity data capture may have clinical benefit in patients with T1D by impacting cardiovascular fitness and optimal body weight achievement and maintenance, limited number of such studies have been conducted to date. Clinical trial registries provide information about a single small sample size 2 center prospective study incorporating physical activity data input to modulate closed-loop control in T1D that are seeking to build on prior studies. We expect an increase in such studies especially since the NIH has expanded support of this type of research with additional grants starting in the second hal...

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Section: General Considerations Important In Translating Physical Actmentioning

confidence: 99%

“…53,54 The signal may also be interfered with by other BTLE devices or malicious interference. Currently as CLC systems are being developed, there is a need to share data within and between sub components of the CLC system and the clinical team.…”

Section: Data Integritymentioning

confidence: 99%

Physical Activity Capture Technology With Potential for Incorporation Into Closed-Loop Control for Type 1 Diabetes

Dadlani

Levine

McCrady-Spitzer

et al. 2015

J Diabetes Sci Technol

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“…However, other sensors are gaining popularity such as the monitor sleep, intradermal variables and heart rate [4]. Thirdly, the cost of developing mass scalable electronics is declining, so the barrier to producing new compact wearable technologies has diminished.…”

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confidence: 99%

A Five-Step Strategy to Combine Data Sources from Multiple Wearable Sensors

Levine¹

2017

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With the multitude of non-communicating wearable sensors, there is an urgent need to better combine wearable data streams in order to improve human health and well-being. A five-step process is proposed. The first step is to specify the human behavior that the data set will address. The second step is to critically assess primary measurement that allows the behavioral goal to be addressed. After this, other streams can be integrated in a hierarchical fashion based on their accuracy, precision and relevance. The third step is to perform a hierarchical synthesis of the multiple data streams. In the fourth step, the multiple data streams are integrated for practical use; we propose achieving this with wearable computers. The final step is that system retraining occurs, via Artificial Intelligence, so that an integrated wearable system can be individualized. A case study of Type 1 diabetes is used: this analysis and the proposed solutions illustrate the need for an urgent interdisciplinary debate to advance useful methods for combining data from divergent wearable sensors. Wearable fully integrated systems, programmed with Artificial Intelligence, will enable data from multiple wearable sensors to be optimized to improve individual well-being.

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“…Introduction and State of the Art Ubiquitous Computing and Ambient Intelligence (Ami) bring forwards innovative opportunities to provide key health services at the Point of Care [1]. The availability of smart monitoring solutions at home can provide medical doctors, physiotherapists and carers with reliable data about people's health status when required.…”

mentioning

confidence: 99%

Personalisation of Intelligent Homecare Services Adapted to Children with Motor Impairments

Valero

Lindén

Velasco

et al. 2014

Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services

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Abstract. Ambient Intelligence could support innovative application domains like motor impairments' detection at the home environment. This research aims to prevent neurodevelopmental disorders through the natural interaction of the children with embedded intelligence daily life objects, like home furniture and toys. Designed system uses an interoperable platform to provide two intelligent interrelated home healthcare services: monitoring of children's abilities and completion of early stimulation activities. A set of sensors, which are embedded within the rooms, toys and furniture, allows private data gathering about the child's interaction with the environment. This information feeds a reasoning subsystem, which encloses an ontology of neurodevelopment items, and adapts the service to the age and acquisition of expected abilities. Next, the platform proposes customized stimulation services by taking advantage of the existing facilities at the child's environment. The result integrates Embedded Sensor Systems for Health at Malardalen University with UPM Smart Home, for adapted services delivery.

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Embedded Sensor Systems for Health in Home Healthcare

Cited by 3 publications

References 29 publications

Physical Activity Capture Technology With Potential for Incorporation Into Closed-Loop Control for Type 1 Diabetes

Physical Activity Capture Technology With Potential for Incorporation Into Closed-Loop Control for Type 1 Diabetes

A Five-Step Strategy to Combine Data Sources from Multiple Wearable Sensors

Personalisation of Intelligent Homecare Services Adapted to Children with Motor Impairments

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