Trust is explored in this thesis through the analysis of the robustness of wearable device based artificial intelligence based models to changes in data acquisition. Specifically changes in wearable device hardware and different recording sessions are explored. Three human activity recognition models are used as a vehicle to explore this: Model A which is trained using accelerometer signals recorded by a wearable sensor referred to as Astroskin, Model H which is trained using accelerometer signals from a wearable sensor referred to as the BioHarness and Model A Type 1 which was trained on Astroskin accelerometer signals that was recorded on the first session of the experimental protocol. On a test set recorded by Astroskin Model A had a 99.07% accuracy. However on a test set recorded by the BioHarness Model A had a 65.74% accuracy. On a test set recorded by BioHarness Model H had a 95.37% accuracy. However on a test set recorded by Astroskin Model H had a 29.63% accuracy. Model A Type 1 an average accuracy of 99.57% on data recorded by the same wearable sensor and same session. An average accuracy of 50.95% was obtained on a test set that was recorded by the same wearable sensor but by a different session. An average accuracy of 41.31% was obtained on data that was recorded by a different wearable sensor and same session. An average accuracy of 19.28% was obtained on data that was recorded by a different wearable sensor and different session. An out of domain discriminator for Model A Type 1 was also implemented. The out of domain discriminator was able to differentiate between the data that trained Model A Type 1 and other types (data recorded by a different wearable devices/different sessions) with an accuracy of 97.60%. / Thesis / Master of Applied Science (MASc) / The trustworthiness of artificial intelligence must be explored before society can fully reap its benefits. The element of trust that is explored in this thesis is the robustness of wearable device based artificial intelligence models to changes in data acquisition. The specific changes that are explored are changes in the wearable device used to record the input data as well as input data from different recording sessions. Using human activity recognition models as a vehicle, the results show that performance degradation occurs when the wearable device is changed and when data comes from a different recording session. An out of domain discriminator is developed to alert users when a potential performance degradation can occur.

abstract: Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The pathology for PD is difficult and expensive. Furthermore, it depends on patient diaries and the neurologist’s subjective assessment of clinical scales. Objective, accurate, and continuous patient monitoring have become possible with the advancement in mobile and portable equipment. Consequently, a significant amount of work has been done to explore new cost-effective and subjective assessment methods or PD symptoms. For example, smart technologies, such as wearable sensors and optical motion capturing systems, have been used to analyze the symptoms of a PD patient to assess their disease progression and even to detect signs in their nascent stage for early diagnosis of PD. This review focuses on the use of modern equipment for PD applications that were developed in the last decade. Four significant fields of research were identified: Assistance diagnosis, Prognosis or Monitoring of Symptoms and their Severity, Predicting Response to Treatment, and Assistance to Therapy or Rehabilitation. This study reviews the papers published between January 2008 and December 2018 in the following four databases: Pubmed Central, Science Direct, IEEE Xplore and MDPI. After removing unrelated articles, ones published in languages other than English, duplicate entries and other articles that did not fulfill the selection criteria, 778 papers were manually investigated and included in this review. A general overview of PD applications, devices used and aspects monitored for PD management is provided in this systematic review. / Dissertation/Thesis / Masters Thesis Computer Engineering 2019

Background Wearable technology, which is a part of the Internet of Things (IoT), appears to be an upcoming trend with increasing importance within the business world. Nevertheless, no clear business model for companies working with wearables had been defined yet taking the influences wearables have on businesses and especially their value proposition into consideration. Purpose The purpose of this thesis is to offer input to the lack of existing literature within business models and wearables technology. The aim is to unfold a general business model that can be used within wearable companies/IoT businesses and show the influence these technologies have on them. Methodology In order to conduct an empirical research a multiple case study has been conducted, based on semi-structured interviews with eight companies, which core business consists out of wearable technology. The frameworks on business models by Gassmann et al (2014) and Osterwalder and Pigneur (2010) serve as the basis for this study and its analysis, which is based on a grounded theory approach. Results It appears that a great amount of similarities can be found through the cross-case analysis between the cases. This makes the construction of a new business model possible. The unfolded model gives also a new contribution to the theory of Hui (2014) regarding a new area of value creation and value capture within IoT businesses.

In recent years, soft materials have seen increased prevalence in the design of robotic systems and wearables capable of addressing the needs of individuals living with disabilities. In particular, pneumatic artificial muscles (PAMs) have readily been employed in place of electromagnetic actuators due to their ability to produce large forces and motions, while still remaining lightweight, compact, and flexible. Due to the inherent nonlinearity of PAMs however, additional external or embedded sensors must be utilized in order to effectively control the overall system. In the case of external sensors, the bulkiness of the overall system is increased, which places limits on the system’s design. Meanwhile, the traditional cylindrical form factor of PAMs limits their ability to remain compact and results in overly complex fabrication processes when embedded fibers and/or sensing elements are required to provide efficient actuation and control. In order to overcome these limitations, this thesis proposed the design of flat pneumatic artificial muscles (FPAMs) capable of being fabricated using a simple layered manufacturing process, in which water-soluble masks were utilized to create collapsed air chambers. Furthermore, hyperelastic deformation models were developed to approximate the mechanical performance of the FPAMs and were verified through experimental characterization. The feasibility of these design techniques to meet the requirements of human centered applications, including the suppression of hand tremors and catheter ablation procedures, was explored and the potential for these soft actuation systems to act as solutions in other real world applications was demonstrated. We expect the design, fabrication, and modeling techniques developed in this thesis to aid in the development of future wearable devices and motivate new methods for researchers to employ soft pneumatic systems as solutions in human-centered applications.

abstract: Current exosuit technologies utilizing soft inflatable actuators for gait assistance have drawbacks of having slow dynamics and limited portability. The first part of this thesis focuses on addressing the aforementioned issues by using inflatable actuator composites (IAC) and a portable pneumatic source. Design, fabrication and finite element modeling of the IAC are presented. Volume optimization of the IAC is done by varying its internal volume using finite element methods. A portable air source for use in pneumatically actuated wearable devices is also presented. Evaluation of the system is carried out by analyzing its maximum pressure and flow output. Electro-pneumatic setup, design and fabrication of the developed air source are also shown. To provide assistance to the user using the exosuit in appropriate gait phases, a gait detection system is needed. In the second part of this thesis, a gait sensing system utilizing soft fabric based inflatable sensors embedded in a silicone based shoe insole is developed. Design, fabrication and mechanical characterization of the soft gait detection sensors are given. In addition, integration of the sensors, each capable of measuring loads of 700N in a silicone based shoe insole is also shown along with its possible application in detection of various gait phases. Finally, a possible integration of the actuators, air source and gait detection shoes in making of a portable soft exosuit for knee assistance is given. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2020

The goal of this master's thesis is to describe the possibilites of devices with operating system Android Wear, there is a description of Android Wear API and components, which are nowadays widely used in smart wearable devices. The thesis contains a description of recognition of dynamic gestures with the use of machine learning methods applied on data, which are provided by a smart device. In the practical part of this master's thesis is described an implemented library, which allows to train gestures and recognize them using FastDTW algorithm and inform a connected device about the recognized movement. Use of the library is shown on a demo application.

In the recent years, there has been a rapid development of miniature sensor based wearable devices with broad functionality. Some of these devices are designed to function continuously for weeks with a single battery charge, which requires their power usage to be as low as possible. This can be achieved either through circuit optimization, a more powerful battery, or through optimization of the software controlling the device. This thesis presents an approach to the latter option by the means of a power and energy simulator based on the Open Virtual Platform (OVP). Such a tool can simulate the embedded software and predict the power and energy usage of the system. The result of the simulator could be used in combination with other tools to optimize the software implementation of the system and to lower its overall power and energy consumption.

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