Use of smartphone-based remote assessments of multiple sclerosis in Floodlight Open, a global, prospective, open-access study

Floodlight Open was a global, open-access, digital-only study designed to understand the drivers and barriers in deployment and use of a smartphone app in a naturalistic setting and broad study population of people with and without multiple sclerosis (MS). The study utilised the Floodlight Open app: a 'bring-your-own-device' solution that remotely measures a user's mood, cognition, hand motor function, and gait and postural stability via smartphone sensor-based tests requiring active user input ('active tests'). Levels of mobility of study participants ('life-space measurement') were passively measured. Study data from these tests were made available via an open-access platform. Data from 1350 participants with self-declared MS and 1133 participants with self-declared non-MS from 17 countries across four continents were included in this report. Overall, MS participants provided active test data for a mean duration of 5.6 weeks or a mean duration of 19 non-consecutive days. This duration increased among MS participants who persisted beyond the first week to a mean of 10.3 weeks or 36.5 non-consecutive days. Passively collected life-space measurement data were generated by MS participants for a mean duration of 9.8 weeks or 50.6 non-consecutive days. This duration increased to 16.3 weeks/85.1 non-consecutive days among MS participants who persisted beyond the first week. Older age, self-declared MS disease status, and clinical supervision as part of concomitant clinical research were all significantly associated with higher persistence of the use of the Floodlight Open app. MS participants performed significantly worse than non-MS participants on four out of seven active tests. The findings from this multinational study inform future research to improve the dynamics of persistence of use of digital monitoring tools and further highlight challenges and opportunities in applying them to support MS clinical care.

Physiological sensor data cleaning with autoencoders

Objective.Physiological sensor data (e.g. photoplethysmograph) is important for remotely monitoring patients' vital signals, but is often affected by measurement noise. Existing feature-based models for signal cleaning can be limited as they might not capture the full signal characteristics.Approach.In this work we present a deep learning framework for sensor signal cleaning based on dilated convolutions which capture the coarse- and fine-grained structure in order to classify whether a signal is noisy or clean. However, since obtaining annotated physiological data is costly and time-consuming we propose an autoencoder-based semi-supervised model which is able to learn a representation of the sensor signal characteristics, also adding an element of interpretability.Main results.Our proposed models are over 8% more accurate than existing feature-based approaches with half the false positive/negative rates. Finally, we show that with careful tuning (that can be improved further), the semi-supervised model outperforms supervised approaches suggesting that incorporating the large amounts of available unlabeled data can be advantageous for achieving high accuracy (over 90%) and minimizing the false positive/negative rates.Significance.Our approach enables us to reliably separate clean from noisy physiological sensor signal that can pave the development of reliable features and eventually support decisions regarding drug efficacy in clinical trials.

Beacon-Based Remote Measurement of Social Behavior in ASD Clinical Trials: A Technical Feasibility Assessment

In this work, we propose a Bluetooth low energy (BLE) beacon-based algorithm to enable remote measurement of the social behavior of the participants of an observational Autism Spectrum Disorder (ASD) clinical trial (NCT03611075). We have developed a mobile application for a smartphone and a smartwatch to collect beacon signals from BLE beacon sensors as well as to store information about the participants’ household rooms. Our goal is to collect beacon information about the time the participants spent in different rooms of their household to infer sociability information. We applied the same technology and setup in an internal experiment with healthy volunteers to evaluate the accuracy of the proposed algorithm in 10 different home setups, and we observed an average accuracy of 97.2%. Moreover, we show that it is feasible for the clinical study participants/caregivers to set up the BLE beacon sensors in their homes without any technical help, with 96% of them setting up the technology on the first day of data collection. Next, we present results from one-week location data from study participants collected through the proposed technology. Finally, we provide a list of good practice guidelines for optimally applying beacon technology for indoor location monitoring. The proposed algorithm enables us to estimate time spent in different rooms of a household that can pave the development of objective sociability features and eventually support decisions regarding drug efficacy in ASD.

A quality metric for heart rate variability from photoplethysmogram sensor data

Heart rate variability (HRV) measures the regularity between consecutive heartbeats driven by the balance between the sympathetic and parasympathetic branches of the autonomous nervous system. Wearable devices embedding photoplethysmogram (PPG) technology can be used to derive HRV, creating many opportunities for remote monitoring of this physiological parameter. However, uncontrolled conditions met in daily life pose several challenges related to disturbances that can deteriorate the PPG signal, making the calculation of HRV metrics untrustworthy and not reliable. In this work, we propose a HRV quality metric that is directly related to the HRV accuracy and can be used to distinguish between accurate and inaccurate HRV values. A parametric supervised approach estimates HRV accuracy using a model whose inputs are features extracted from the PPG signal and the output is the HRV error between HRV metrics obtained from the PPG and the ECG collected during an experimental protocol involving several activities. The estimated HRV accuracy of the model is used as an indication of the HRV quality.

SampleLite

We consider the link adaptation problem in 802.11n wireless LANs that involves adapting MIMO mode, channel bonding, modulation and coding scheme, and frame aggregation level with varying channel conditions. Through measurement-based analysis, we find that adapting all available 802.11n features results in higher goodput than adapting only a subset of features, thereby showing that holistic link adaptation is crucial to achieve best performance. We then design a novel hybrid link adaptation scheme termed SampleLite that adapts all 802.11n features while being efficient compared to sampling-based open-loop schemes and practical relative to closed loop schemes. SampleLite uses sender-side RSSI measurements to significantly lower the sampling overhead, by exploiting the monotonic relationship between best settings for each feature and the RSSI. Through analysis and experimentation in a testbed environment, we show that our proposed approach can reduce the sampling overhead by over 70% on average compared to the widely used Minstrel HT scheme. We also experimentally evaluate the goodput performance of SampleLite in a wide range of controlled and real-world interference scenarios. Our results show that SampleLite, while performing close to the ideal, delivers goodput that is 35-100% better than with existing schemes.