As sensor networks for health monitoring become more prevalent, so will the need to control their usage and consumption of energy. This paper presents a method which leverages the algorithm's performance and energy consumption. By utilising Reinforcement Learning (RL) techniques, we provide an adaptive framework, which continuously performs weak training in an energy-aware system. We motivate this using a realistic example of residential localisation based on Received Signal Strength (RSS). The method is cheap in terms of work-hours, calibration and energy usage. It achieves this by utilising other sensors available in the environment. These other sensors provide weak labels, which are then used to employ the State-Action-Reward-State-Action (SARSA) algorithm and train the model over time. Our approach is evaluated on a simulated localisation environment and validated on a widely available pervasive health dataset which facilitates realistic residential localisation using RSS. We show that our method is cheaper to implement and requires less effort, whilst at the same time providing a performance enhancement and energy savings over time.
@cite_21: Recent advances in embedded technology have enabled more pervasive machine learning. One of the common applications in this field is Egocentric Activity Recognition (EAR), where users wearing a device such as a smartphone or smartglasses are able to receive feedback from the embedded device. Recent research on activity recognition has mainly focused on improving accuracy by using resource intensive techniques such as multi-stream deep networks. Although this approach has provided state-of-the-art results, in most cases it neglects the natural resource constraints (e.g. battery) of wearable devices. We develop a Reinforcement Learning model-free method to learn energy-aware policies that maximize the use of low-energy cost predictors while keeping competitive accuracy levels. Our results show that a policy trained on an egocentric dataset is able use the synergy between motion and vision sensors to effectively tradeoff energy expenditure and accuracy on smartglasses operating in realistic, real-world conditions.
@cite_0: Modern wearable devices are equipped with increasingly powerful microcontrollers and therefore are increasingly capable of doing computationally heavy operations, such as feature extraction from sensor data. This paper quantifies the time and energy costs required for on-board computation of features on acceleration data, the reduction achieved in subsequent communication load, and the impact on daily activity recognition in terms of classification accuracy. The results show that platforms based on modern 32-bit ARM Cortex-M microcontrollers significantly benefit from on-board extraction of time-domain features. On the other hand, efficiency gains from computation of frequency domain features at the moment largely remain out of their reach.
@cite_2: Wearable technologies are valuable tools that can encourage people to monitor their own well-being and facilitate timely health interventions. In this paper, we present SPW-2; a low-profile versatile wearable sensor that employs two ultra low power accelerometers and an optional gyroscope. Designed for minimum maintenance and a long-term operation outside the laboratory, SPW-2 is able to offer a battery lifetime of multiple months. Measurements on its wireless performance in a real residential environment with thick brick walls, demonstrate that SPW-2 can fully cover a room and - in most cases - the adjacent room, as well.
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@cite_16: This paper shares experiences and lessons learned from designing and developing the bespoke Internet of Things (IoT) sensing platforms of a sensor platform for healthcare in a residential environment (SPHERE). These IoT platforms, which include a wrist-worn wearable sensor, a room-level environmental sensor, and a dual-radio gateway, constitute integral parts of the SPHERE system that is currently getting deployed in up to 100 houses of volunteers for up to 12 months. This paper is focused on sharing several years’ worth of insight and experiences from making IoT sensing platforms for large-scale academic deployments, providing information that can accelerate the development and deployment of prototypes and enable timely research in the space of IoT.
The available pervasive health monitoring sensors, such as PIR or ES @cite_16 differ in their usability and the quality of their readings. They also differ in how much energy it takes to operate them and process their results @cite_0 . Low-power wearable sensors @cite_2 are also popular within the community, providing not only the on-board acceleration observations, but also acting as a RF anchor. The fusion of these sensors @cite_5 show that different combinations can provide improvement to localisation and activity recognition @cite_21 performance.