A Review of Safety and Design Requirements of the Artificial Pancreas

Blauw, Helga; Keith-Hynes, Patrick; Koops, Robin; DeVries, J. Hans

doi:10.1007/s10439-016-1679-2

Cited by 56 publications

(41 citation statements)

References 77 publications

(134 reference statements)

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“…Although the hypo-or hyper-glycemic conditions are the most unsafe, there are numerous other faults and errors that potentially lead to failure modes. Safety issues related to each component and the complete AP system are described in [137]. Faults related specifically to the insulin pump operation and communication channels have been studied in [138][139][140].…”

Section: Known and Foreseeable Hazards Associated With The Operation mentioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

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Significant increases in processing power, coupled with the miniaturization of processing units operating at low power levels, has motivated the embedding of modern control systems into medical devices. The design of such embedded decision-making strategies for medical applications is driven by multiple crucial factors, such as: (i) guaranteed safety in the presence of exogenous disturbances and unexpected system failures; (ii) constraints on computing resources; (iii) portability and longevity in terms of size and power consumption; and (iv) constraints on manufacturing and maintenance costs. Embedded control systems are especially compelling in the context of modern artificial pancreas systems (AP) used in glucose regulation for patients with type 1 diabetes mellitus (T1DM). Herein, a review of potential embedded control strategies that can be leveraged in a fully-automated and portable AP is presented. Amongst competing controllers, emphasis is provided on model predictive control (MPC), since it has been established as a very promising control strategy for glucose regulation using the AP. Challenges involved in the design, implementation and validation of safety-critical embedded model predictive controllers for the AP application are discussed in detail. Additionally, the computational expenditure inherent to MPC strategies is investigated, and a comparative study of runtime performances and storage requirements among modern quadratic programming solvers is reported for a desktop environment and a prototype hardware platform.

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Section: Known and Foreseeable Hazards Associated With The Operation mentioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

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“…28 In such a system, it is necessary for decisions to be made locally with a processor located either worn by or carried by the patient (or somehow in the vicinity of the patient at all times) rather than by a cloud-based server which is going to suffer from potential delays in processing or temporary data dropout in case of data transmission problems. 29 Artificial pancreas systems in use and under development all use fog or edge computing systems.…”

Section: Diabetes Devices Currently Using Fog Computingmentioning

confidence: 99%

Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things

Klonoff

2017

J Diabetes Sci Technol

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The Internet of Things (IoT) is generating an immense volume of data. With cloud computing, medical sensor and actuator data can be stored and analyzed remotely by distributed servers. The results can then be delivered via the Internet. The number of devices in IoT includes such wireless diabetes devices as blood glucose monitors, continuous glucose monitors, insulin pens, insulin pumps, and closed-loop systems. The cloud model for data storage and analysis is increasingly unable to process the data avalanche, and processing is being pushed out to the edge of the network closer to where the datagenerating devices are. Fog computing and edge computing are two architectures for data handling that can offload data from the cloud, process it nearby the patient, and transmit information machine-to-machine or machine-to-human in milliseconds or seconds. Sensor data can be processed near the sensing and actuating devices with fog computing (with local nodes) and with edge computing (within the sensing devices). Compared to cloud computing, fog computing and edge computing offer five advantages: (1) greater data transmission speed, (2) less dependence on limited bandwidths, (3) greater privacy and security, (4) greater control over data generated in foreign countries where laws may limit use or permit unwanted governmental access, and (5) lower costs because more sensor-derived data are used locally and less data are transmitted remotely. Connected diabetes devices almost all use fog computing or edge computing because diabetes patients require a very rapid response to sensor input and cannot tolerate delays for cloud computing.

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“…Presently, however, only subcutaneous interstitial enzyme glucose sensors are at TRL9 and so practical for this purpose (with wireless data transmission-enhancing practicality). These sensors generate an electric current proportional to local glucose concentration, and through a calibration procedure, this current is converted into an estimated blood glucose value [64]. Interstitial glucose measurement suffers from a certain delay between changes in blood glucose and interstitial fluid as well as signal drift over time and gradual encapsulation of the sensor (which implies period placement of a new sensor).…”

Section: Ckd In Connection With Diabetes-related Parameters (6/23)mentioning

confidence: 99%

“…The rapid developments in this area imply a need for consensus about safety requirements and recognized methods for performance test and comparison of AP devices, and indeed, excellent studies on this already have been published [64,72]. Recently, the Diabetes Technology Society published a standard for Wireless Diabetes Device Security (DTSec version 1.0).…”

Section: Ckd In Connection With Diabetes-related Parameters (6/23)mentioning

confidence: 99%

Wearable sensors: can they benefit patients with chronic kidney disease?

Wieringa

Broers

Kooman

et al. 2017

Expert Review of Medical Devices

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Introduction: This article ponders upon wearable medical measurement devices in relation to Chronic Kidney Disease (CKD) and its' associated comorbidities -and whether these might benefit CKD-patients. We aimed to map the intersection(s) of nephrology and wearable sensor technology to help technologists understand medical aspects, and clinicians to understand technological possibilities that are available (or soon will become so). Areas covered: A structured literature search on main comorbidities and complications CKD patients suffer from, was used to steer mini-reviews on wearable sensor technologies clustered around 3 themes being: Cardiovascular-related, diabetes-related and physical fitness/frailty. This review excludes wearable dialysis -although also strongly enabled by miniaturization -because that highly important theme deserves separate in-depth reviewing. Expert commentary: Continuous progress in integrated electronics miniaturization enormously lowered price, size, weight and energy consumption of electronic sensors, processing power, memory and wireless connectivity. These combined factors boost opportunities for wearable medical sensors. Such devices can be regarded as enablers for: Remote monitoring, influencing human behaviour (exercise, dietary), enhanced home care, remote consults, patient education and peer networks. However, to make wearable medical devices succeed, the challenge to fit them into health care structures will be dominant over the challenge to realize the bare technologies themselves. ARTICLE HISTORY

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A Review of Safety and Design Requirements of the Artificial Pancreas

Cited by 56 publications

References 77 publications

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things

Wearable sensors: can they benefit patients with chronic kidney disease?

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