3 research outputs found
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Activity recognition in a home setting using off the shelf smart watch technology
Being able to detect in real-time the activity per- formed by a user in a home setting provides highly valuable context. It can allow more effective use of novel technologies in a large variety of applications, from comfort and safety to energy efficiency, remote health monitoring and assisted living. In a home setting, activity recognition has been traditionally studied based on either a large sensor network infrastructure already set up in a home, or a network of wearable sensors attached to various parts of the user’s body. We argue that both approaches suffer considerably in terms of practicality and propose instead the use of commercial off-the-shelf smart watches, already owned by the users. We test the feasibility of this approach with two different smart watches of very different capabilities, on a variety of activities performed daily in a domestic environment, from brushing teeth to preparing food. Our experimental results are encouraging, as using stan- dard Support Vector Machine based classification, the accuracy rates range between 88% and 100%, depending on the type of smart watch and the window size chosen for data segmentation
Location-enhanced activity recognition in indoor environments using off the shelf smart watch technology and BLE beacons
Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation
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Towards power of preemption on parallel machines
Classical scheduling models typically fall in either of two categories: those that allow interruption of the processing of jobs, and those that do not. In parallel machine environments, scheduling problems for models which allow parallel processing of jobs are typically easier to solve, in terms of computational requirements, while these models are in the majority of cases associated with an improved quality of solutions.
In this thesis, we focus on one of the notion of preemption, a foundational concept in scheduling, which defines the ability to interrupt the processing of a job and resuming it at a later time, or in the case of multiple processors, on a different machine. Preemptive scheduling is limited to the fact that every job in a preemptive schedule may not be processed by more than one machine at a time. Additionally, we consider the closely related notion of splitting jobs, where jobs can be processed at the same time by multiple processors.
We address the issue of power of preemption and power of splitting, defined as the ratio of the cost function of an optimal non-preemptive schedule over the cost function of an optimal preemptive schedule, and schedule with splitting jobs respectively. For several parallel machine scheduling models we provide new results, in addition to a detailed review of the best known results