Functional capacity testing is essential for assessing mobility changes, which can impact independence across various populations and health conditions. This study aims to implement instrumented function tests using a combination of affordable sensors, including sensorized mats, sensorized shoes, smartphones, and smartwatches. The goal is to provide objective, reliable, and detailed data on test outcomes, such as gait analysis. We have created a dataset from 6 participants of varying ages, each performing 5 standardized functional tests: Timed Up and Go, 30-Second Chair Rise, Locomo challenge, 10-meter walk, and 40-meter walk. Alongside the dataset, we have developed a tool for visualizing the sensor signals and marking key events to facilitate data analysis. This dataset is intended to support researchers in developing algorithms for extracting test-specific parameters, and for comparing sensors in terms of quality of the signals and ease of setup.

The Timed Up and Go (TUG) test is a widely recognized and standardized mobility test to measure basic mobility and balance capabilities. Despite the possibility to derive rich information about the patient, only the total time to complete the test is conventionally measured by a professional. This work examines the use of non-wearable sensors for the measurement of parameters of the test in an accurate and objective way. The study illustrates a system specifically designed for conducting the TUG test using a set of sensorized mats. The developed system is able to identify the following 4 phases of the test, with relative timestamps: TUG-time, Sit-to-Stand, Mid-Turning, and End-Turning-Stand-to-Sit. Additionally, meaningful parameters for gait assessment are also extracted: walking speed and stride length. Two experimental iterations were conducted to assess the reliability of the developed software. Both iterations involved two different groups of six healthy participants (41.58 ± 13.32 yrs; 6 females, 6 males) performing various walking types. Our results demonstrate that sensorized mats can be used to segment the phases of the test reliably and can additionally be used to quantify gait parameters during the walk phase of the test.

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.