Wearable computer

Summary

A wearable computer, also known as a body-borne computer, is a computing device worn on the body. The definition of 'wearable computer' may be narrow or broad, extending to smartphones or even ordinary wristwatches. Wearables may be for general use, in which case they are just a particularly small example of mobile computing. Alternatively, they may be for specialized purposes such as fitness trackers. They may incorporate special sensors such as accelerometers, heart rate monitors, or on the more advanced side, electrocardiogram (ECG) and blood oxygen saturation (SpO2) monitors. Under the definition of wearable computers, we also include novel user interfaces such as Google Glass, an optical head-mounted display controlled by gestures. It may be that specialized wearables will evolve into general all-in-one devices, as happened with the convergence of PDAs and mobile phones into smartphones. Wearables are typically worn on the wrist (e.g. fitness trackers), hung from the neck (lik

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https://en.wikipedia.org/wiki/Wearable_computer

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Remote health monitoring has attracted a lot of attention over the past decades to provide the opportunity for early detection of pathological health conditions. This early detection improves the quality of life for the patients and significantly reduces the burden on their family members. Moreover, this improvement reduces the socioeconomic consequences that are caused due to the disability of patients to work despite their health conditions. Wearable technologies offer a promising solution in pervasive health monitoring by relaxing the constraints concerning time and location. There are two major challenges in developing these technologies. The first challenge is real-time monitoring of the patients to fulfill early detection of life-threatening conditions using advanced machine learning techniques. As a solution for this issue, in this thesis, I considered a paradigm shift toward self-aware approaches and frameworks, which resulted in less energy consumption and longer battery lifetime, hence enabling ambulatory patient monitoring. I provided the possibility of switching between energy-efficient and high-performance modes in these systems. The second challenge is improving performance while guaranteeing the accuracy of the system. In this work, I have addressed this second challenge by combining multi-parametric bio-signals analysis and personalizing the detection for each patient. Throughout this thesis, I focused on epileptic seizure detection systems as a principal case study to analyze and evaluate the proposed techniques.

EPFL2022

Prediction of patient-reported physical activity scores from wearable accelerometer data: a feasibility study

Inès Bahej, Martin Jaggi

Many diseases are characterized by limitations in mobility, including a wide range of musculoskeletal and neurological conditions. Reduced mobility impacts a patients ability to perform activities of daily living, which in turn reduces health-related quality of life. Mobility can be assessed by collecting patient-reported outcome scores from standardized questionnaires and by directly measuring physical activity parameters from wearable accelerometer data. In this work, we explored the relationship between subjectively and objectively measured mobility by training machine learning models to predict patient responses based on features derived from real-world acceleration data. Our method achieved up to 82% accuracy using a random forest classifier and set the basis to develop novel data-driven digital biomarkers for objective, quantitative and more frequent evaluation of patients’ mobility.

2018

MBioTracker: Multimodal Self-Aware Bio-Monitoring Wearable System for Online Workload Detection

Adriana Arza Valdes, David Atienza Alonso, Rodrigo Mariño Andrés, Una Pale

Cognitive workload affects operators' performance principally in high-risk or time-demanding situations and when multitasking is required. An online cognitive workload monitoring system can provide valuable inputs to decision-making instances, such as the operator's state of mind and resulting performance. Therefore, it can allow potential adaptive support to the operator. This work presents a new design of a wearable embedded system for online cognitive workload monitoring. This new wearable system consists of, on the hardware side, a multi-channel physiological signals acquisition (respiration cycles, heart rate, skin temperature, and pulse waveform) and a low-power processing platform. Further, on the software side, our wearable embedded system includes a novel energy-aware bio-signal processing algorithm. We also use the concept of application self-awareness to enable energy-scalable embedded machine learning algorithms and methods for online subjects' cognitive workload monitoring. Our results show that this new wearable system can continuously monitor multiple bio-signals, compute their key features, and provide reliable detection of high and low cognitive workload levels with a time resolution of 1 minute and a battery lifetime of 14.58h in our experimental conditions. It achieves a detection accuracy of 76.6% (2.6% lower than analogous offline computer-based analysis) with a sensitivity of 77.04% and a specificity of 81.75%, on a simulated drone rescue mission task. Moreover, by applying our self-aware monitoring to exploit different energy-scalable modes, we can increase battery lifetime by 51.6% (up to 22.11 hours) while incurring an insignificant accuracy loss of 1.07%.

2021