The Timed-Up and Go test is a simple yet effective test used to evaluate balance and mobility in conditions that affect movement, such as Parkinson’s disease. This test can inform clinicians about the monitoring and progression of the disease by measuring the time taken to complete the test. We used a smartphone app to obtain the phone’s inertial data and implemented an algorithm to automatically extract the time taken to complete the test. We considered data collected from six healthy participants performing tests at different speeds. The proposed method was further tested on twelve participants with Parkinson’s disease based on a reference measurement in clinic. We show that, for both groups, we obtain good accuracy (RMSE = 3.42 and 1.95 s) and a strong positive correlation (r = 0.85 and 0.83) between estimated duration and ground truth. We highlight limitations in our approach when the test is performed at very low speed or without a clear pause between the test and the user interaction with the phone. 

The 6-minute walk test is a standardized test used in healthcare to monitor the progress of diseases affecting physical capacity and function. Inertial sensors and positioning data from wearables or smartphones allow to conduct clinical tests in patients' home environments, thereby easing the burden for patients and reducing costs for healthcare. Computation of the 6-minute walked distance requires high accuracy to be clinically useful and current consumer technology-based approaches show that noise and interference in the data often misleads algorithms used to estimate the walk distance. In this research, we are sharing a dataset of inertial and positioning information from 203 walking tests collected with users' own smartphones and the respective ground truth distances. Ground truth is measured with trundle wheels of two types, one which only provides the final distance measurement, and one which provides continuous distance measurements to also capture changes in walking speed. The tests are performed by 19 individuals, both cardiovascular patients and healthy participants. We analyse the dataset using a state-of-the-art algorithm and observe algorithm results in relation to walking features. Based on this, we elaborate on for technology development that may provide further improvements in accuracy for walk distance estimation algorithms, including how data quality and reliability can be assessed.

This paper addresses the need for accurate and continuous measurement of walked distance in applications such as indoor localisation, gait analysis or the 6-minute walk test (6MWT). We propose a method to continuously collect ground truth data of walked distance using an IoT-based trundle wheel. The wheel is connected via Bluetooth Low Energy to a smartphone application which allows the collection of inertial sensor data and GPS location information in addition to the reference distance. We prove the usefulness of this data collection approach in a use case where we derive walked distance from inertial data. We train a 1-dimensional CNN on inertial data collected by one researcher in 15 walking sessions of 1 km length at varying speeds. The training is facilitated by the continuous nature of the reference data. The accuracy of the algorithm is then tested on holdout data of a 6-min duration for which the error of the inferred distance is within clinically significant limits. The proposed approach is useful for the efficient collection of input and reference data for the development of algorithms used to estimate walked distance, such as for the 6MWT. 

As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.

Walking speed and distance are usually collected when performing clinical tests such as the 6-Minute Walk Test (6MWT). Wearable devices and smartphones can help bring these tests to the home environment. However, there are difficulties in obtaining measures of distance indoors, where GPS cannot be relied on. Step counting is another even simpler form of data collection that can be obtained through digital technologies. In this work, we investigate the relationship between the step count variable and the standardised 6-Minute Walk Distance (6MWD) variable. By considering 176 6MWTs from 55 participants, we found a high correlation between ground truth distance and the number of steps taken during a test (0.83). Additionally, when considering low-quality outdoor tests, using the step count becomes significantly more reliable (MAE of 22.5m) compared to a state-of-the-art algorithm (MAE of 93.8m). We conclude that step count can be considered as a valid proxy to estimate 6MWD and a candidate approach for monitoring patients’ physical health in free-living conditions.

This work aims to link finger tapping and drawing tests performed on mobile phone screens with clinical ratings of Parkinson's Disease (PD). Thirty PD patients were recruited and instructed to carry out these tests in their homes. Features were extracted and used to assess the validity of the data vis a vis clinical scales (MDS-UPDRS). Statistical tests show that several features correlate with clinical scores (max correlation 0.54) and significant differences between data collected before and after medication intake (p<0.05), demonstrating the clinical validity of smartphone data. The use of Machine Learning (ML) algorithms to regress Part-3 of MDS-UPDRS further supports the validity with an absolute mean error of 6.25 (over a 0-72 scale).

This paper investigates usability aspects of a mobile application aimed at monitoring symptoms of Parkinson’s disease (PD) patients. Thirty PD patients collected data through mobile-based questionnaires and activity tasks aimed at measuring motor and non-motor symptoms for a duration of two months. We report the results about usability conducted within this study. A combination of methods consisting of the uMARS questionnaire and interviews with PD patients inform the usability aspects of the mobile application. Results indicate that the app is overall received well and is usable (median uMARS score=4). Interviews reveal usability issues related to the size of textual instructions and buttons, and to the context of use of the app, particularly when the phone is used as a sensor. These findings highlight the need of co-design and preliminary testing when developing apps for PD.

Neuropathic pain is a difficult condition to treat and would require reliable biomarkers to personalise and optimise treatments. To date, pain levels are mostly measured with subjective scales, but research has shown that electroencephalography (EEG) and heart rate variability (HRV) can be linked to those levels. Internet of Things technology could allow embedding EEG and HRV in easy-to-use systems that patients can use at home in their daily life. We have developed a system for home monitoring that includes a portable EEG device, a tablet application to guide patients through imaginary motor tasks while recording EEG, a wearable HRV sensor and a mobile phone app to report pain levels. We are using this system in a clinical study involving 15 spinal cord injury patients for one month. Preliminary results show that relevant data are being collected, with inter and intra-patients variability for both HRV and pain levels, and that the mobile phone app is perceived as usable, of good quality and useful. However, because of its complexity, the system requires some effort from patients, is sometimes unreliable and the collected EEG signals are not always of the desired quality.

Asthma monitoring is an important aspect of patient self-management. However, due to its repetitive nature, patients can find long-term monitoring tedious. Mobile health can provide an avenue to monitor asthma without needing high levels of active engagement, and instead rely on passive monitoring. In our recent AAMOS-00 study, we collected mobile health data over six months from 22 asthma patients using passive and active monitoring technology, including smartwatch, peak flow measurements, and daily asthma diaries.

Compliance to smartwatch monitoring was found to lie between the compliance to complete daily asthma diaries and measuring daily peak flow. However, some study participants faced technical issues with the devices which could have affected the relative compliance of the monitoring tasks.

Moreover, as evidenced by standard usability questionnaires, we found that the AAMOS-00 study’s data collection system was similar in quality to other studies and published apps.