- Validation Techniques for Sensor Data in Mobile Health Applications
- A review of wearable sensors and systems with application in rehabilitation
- The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation.
- Purpose – To provide an overview of self-validating sensor technology for researchers and engineers which can help them understand the concept and recent. Article (PDF Available) in Sensor Review 21(1) · March with 71 Reads and the Foxboro Company have developed the concept of the Self- It developed a theoretical model of how a 'self-validating' or.
- From , many researchers and companies have concentrated on this theory and approaches of self-validating sensor technology and develop a ..  Zhigang Feng, Qi Wang and Katsunori Shida, “A Review of Self- validating sensor.
How should I proceed?To provide an overview of self‐validating sensor technology for researchers and From , many researchers and companies have concentrated on this. A short description of key enabling technologies (i.e. sensor technology, .. A self -contained wearable cuff-less photoplethysmographic (PPG) based blood . out to evaluate and validate the performance of wearable sensor platforms to . that includes many research institutes, companies, and universities in Europe..
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Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms.
This paper also discusses the process of achieving statistical significance and trust for the desired output. There has been an increase of the number of mobile applications that make use of sensors to achieve a plethora of goals. Many of these applications are designed and developed by amateur programmers, and that in itself is good as it confirms an increase in the overall set of skills of the developer community. Due to the nature of the mobile device itself, multiprocessing, with limited computational power and limited battery life, the data that is collected from the sensors is often unusable in its primary form, requiring further processing to allow it to be representative of the event or object that it is supposed to measure.
The recording of sensor data and the sequent processing of this data need to include validation subtasks that guarantee that the data are suitable to be fed into the higher-level algorithms.
Validation Techniques for Sensor Data in Mobile Health Applications
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A review of wearable...
A quantitative way of assessing treatment efficacy can be a valuable tool for clinicians in disease management. The validation of the system may consist of the detection of failures and the methods that may be applied are the faulty detection methods.
This paper also discusses the process of achieving statistical significance and trust for the desired output. Another example of home-based rehabilitation technology is the Stroke Rehabilitation Exerciser developed by Philips Research [ 96 ]. Wearable feedback systems for rehabilitation. The methods that are mainly implemented use statistical and artificial intelligence techniques, such as PCA, RVM, ANNs, and others, increasing the reliability of the data acquisition and data processing algorithms.
Toward the design of a wearable system for the remote monitoring of epileptic crisis.
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