Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

Abstract

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution.

Analysis of the Results of Heel-Rise Test with Sensors: A Systematic Review

“…The inertial sensors embedded on mobile devices may help in the measurement of the results of the Heel-Rise Test. Previously, we proposed the creation of a personal digital life coach for the diagnostics of physical therapy [50]. The implementation of machine learning methods allows the creation of more accurate methods, and the development of these methods with technological equipment may increase the commodity and people's health.…”

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks

“…Such pre-processing can include basic validation of the user inputs by making sure that they are within reasonable ranges, or more advanced processing based on machine learning, signal processing, or time series analysis algorithms. For example, such methods can be implemented within a mobile application on the edge to identify patterns and measure the results of various functional tests, such as the Heel-Rise test [ 33 ], Sit-to-Stand test [ 34 ], Functional Reach Test [ 35 ], or the Timed Up and Go test [ 36 ]. In some cases, these tests’ results could be directly calculated within the mobile app and then provided as already-processed results to the cloud.…”

Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults

“…Several studies [20][21][22] identified the different stages of the TUG test using the actual TUG test or similar settings, but we aimed to estimate the TUG test using normal ground walking with daily-life settings. Saporito et al introduced a remote mobility monitoring method by estimating TUG from free-living activities [23].…”

“…In many studies, various sensor locations were used for health and mobility assessment [5,[12][13][14][15][19][20][21][22][23][24]. Obviously, a wrist type of sensor is less obtrusive and more user convenient.…”