Modelling the recovery effect in batteries and supercapacitors for wearable sensors: discovering the existence of hidden time constants

Wearable devices, including health care monitors and aids, are very popular and extremely pervasive. They allow a user to sense physiological parameters and movement, can process sensed data to derive contextualised information, and also communicate with the wider world to allow remote health monitoring and enable health interventions. The inevitable march of technological invention pushes wearables to have more functionality, process more and sense more. As such, the demands on their power source increases. However, users demand small and lightweight wearable devices which place physical constraints on the power source, thus limiting the available power. To address these two conflicting positions, it seems sensible to consider ways to manage the wearable power source intelligently. It becomes absolutely vital to effectively utilise all the available power for device longevity between charges. Rechargeable batteries are popular in wearable devices. While rechargeable batteries have good energy density, their charge rate can be limited and they can be relatively heavy. Supercapacitors are likely to also be adopted as power sources for wearable sensors; in particular where the sensor mechanism relies on energy harvesting. A specific advantage of supercapacitors over batteries is their maintained performance over large numbers of discharge cycles and they are relatively light weight. It is known in the literature that the electrochemical recovery effect can enable the extraction of more power from the battery when implementing idle times in between discharge cycles, and it has been used to develop various power management techniques. However, there is no evidence concerning the actual increase in available power that can be obtained by exploiting the recovery effect. Also, this property cannot be generalised across all battery chemistries since it is an innate phenomenon, relying on the anode/cathode material. Indeed recent developments suggest that recovery effect does not exist at all. This thesis examines the recovery effect in batteries and presents controlled experiments and results, to verify the presence, and level, of the recovery effect in commonly used battery chemistries that are typically found in wearable sensors and healthcare devices. While the literature analysed the recovery effect using active current and zero discharge current, this work has identified that wearable devices still have a small current drawn from the power source when in idle mode, therefore a novel active and idle discharge circuit was designed to model the recovery effect in the typical operation of wearable devices. The results have revealed that the recovery effect significantly does exist in certain batteries, and a novel contribution from this research has been the identification that the recovery response can be modelled using two different time constants. The time constants reflect the difference in charge carrier movement from the available charge well and the bounded charge well leading to the proposal from this work to model the recovery effect using a two-tank model. This novel finding has important implications for the development of power management techniques that utilise the recovery effect, with application in a large range of battery operated devices. Furthermore, this thesis also has examined the recovery effect in supercapacitors and has for the first time demonstrated that the recovery effect also exists in supercapacitors

Comparison of low-power wireless communication technologies for wearable health-monitoring applications

Health monitoring technologies such as Body Area Network (BAN) systems has gathered a lot of attention during the past few years. Largely encouraged by the rapid increase in the cost of healthcare services and driven by the latest technological advances in Micro-Electro-Mechanical Systems (MEMS) and wireless communications. BAN technology comprises of a network of body worn or implanted sensors that continuously capture and measure the vital parameters such as heart rate, blood pressure, glucose levels and movement. The collected data must be transferred to a local base station in order to be further processed. Thus, wireless connectivity plays a vital role in such systems. However, wireless connectivity comes at a cost of increased power usage, mainly due to the high energy consumption during data transmission. Unfortunately, battery-operated devices are unable to operate for ultra-long duration of time and are expected to be recharged or replaced once they run out of energy. This is not a simple task especially in the case of implanted devices such as pacemakers. Therefore, prolonging the network lifetime in BAN systems is one of the greatest challenges. In order to achieve this goal, BAN systems take advantage of low-power in-body and on-body/off-body wireless communication technologies. This paper compares some of the existing and emerging low-power communication protocols that can potentially be employed to support the rapid development and deployment of BAN systems

Analysis of recovery effect in supercapacitors for wearable devices

Supercapacitors are likely to be adopted as power sources for wearable sensors; in particular where the sensor mechanism relies on energy harvesting. A specific advantage of supercapacitors over traditional batteries is their performance over large numbers of discharge cycles. Likewise, in the case of wearable devices, it is essential to efficiently manage the available power. Supercapacitors exhibit a small recovery effect, in part due to ion diffusion. Modelling this effect allows an increase in available energy to be realized following the sleep times during a discharge cycle thus increasing the time between charging for wearables. This paper presents the increase in useful lifetime that can be achieved via the recovery effect in a typical wearable device

Suitability of external controls for drug evaluation in Duchenne muscular dystrophy

OBJECTIVE: To evaluate the suitability of real-world data (RWD) and natural history data (NHD) for use as external controls in drug evaluations for ambulatory Duchenne muscular dystrophy (DMD). METHODS: The consistency of changes in the 6-minute walk distance (Δ6MWD) was assessed across multiple clinical trial placebo arms and sources of NHD/RWD. Six placebo arms reporting 48-week Δ6MWD were identified via literature review and represented 4 sets of inclusion/exclusion criteria (n = 383 patients in total). Five sources of RWD/NHD were contributed by Universitaire Ziekenhuizen Leuven, DMD Italian Group, The Cooperative International Neuromuscular Research Group, ImagingDMD, and the PRO-DMD-01 study (n = 430 patients, in total). Mean Δ6MWD was compared between each placebo arm and RWD/NHD source after subjecting the latter to the inclusion/exclusion criteria of the trial for baseline age, ambulatory function, and steroid use. Baseline covariate adjustment was investigated in a subset of patients with available data. RESULTS: Analyses included ∼1,200 patient-years of follow-up. Differences in mean Δ6MWD between trial placebo arms and RWD/NHD cohorts ranged from -19.4 m (i.e., better outcomes in RWD/NHD) to 19.5 m (i.e., worse outcomes in RWD/NHD) and were not statistically significant before or after covariate adjustment. CONCLUSIONS: We found that Δ6MWD was consistent between placebo arms and RWD/NHD subjected to equivalent inclusion/exclusion criteria. No evidence for systematic bias was detected. These findings are encouraging for the use of RWD/NHD to augment, or possibly replace, placebo controls in DMD trials. Multi-institution collaboration through the Collaborative Trajectory Analysis Project rendered this study feasible

Energy-Aware Fisheye Routing (EA-FSR) algorithm for wireless mobile sensor networks

Energy consumption is prominent and critical issue faced by wireless sensor networks. The maximum amount of energy is consumed when the sensors communicate with each other. Therefore energy efficient routing mechanisms are required. In this paper, a routing scheme based on the fisheye state routing with a difference in route selection mechanism has been proposed to ensure the reduction in the overall energy consumption of the network. This scheme is named as Energy-Aware Fisheye State Routing (EA-FSR). It is simulated considering various parameters using QualNet5.0. Performance of EA-FSR has been compared with the original fisheye state routing algorithm which is also simulated in the same environment. For comparison various parameters like end-to-end delay average, energy consumption and throughput have been considered