MOBIUS: Smart Mobility Tracking with Smartphone Sensors

Mitri, Daniele Di; Sanusi, Khaleel Asyraaf Mat; Trebing, Kevin; Bromuri, Stefano

doi:10.1007/978-3-030-76063-2_31

Cited by 2 publications

(12 citation statements)

References 13 publications

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“…The most common factors examined were physical activity/mobility [ 2 , 3 , 10 , 11 , 22 , 23 , 49 , 50 , 51 , 52 , 53 ] and sociability [ 3 , 9 , 54 , 55 ]. Sensor data from the accelerometers, gyroscope, and location data (using GPS, Bluetooth) were used to infer factors such as places visited [ 50 ], amount of time spent at home [ 22 ], types of activity performed (sitting, standing, walking, running) [ 11 ] and regularity of movement [ 3 ].…”

Section: Resultsmentioning

confidence: 99%

“…Sensor data from the accelerometers, gyroscope, and location data (using GPS, Bluetooth) were used to infer factors such as places visited [ 50 ], amount of time spent at home [ 22 ], types of activity performed (sitting, standing, walking, running) [ 11 ] and regularity of movement [ 3 ]. On the other hand, location data [ 53 ], frequency and duration of social media app use [ 55 ], microphone recordings [ 3 ] and phone-call and text-message logs [ 54 ] were used to provide information about a person’s physical and virtual sociability.…”

Section: Resultsmentioning

confidence: 99%

“…Although the questions are not standardized across studies (unlike clinical questionnaires), these too are useful in understanding the user contexts. Moreover, self-reports have been used to add additional context by ‘labeling’ passively sensed data and obtaining ground truth [ 11 , 53 , 57 , 88 ], which aids in prediction processes using machine learning.…”

Section: Resultsmentioning

confidence: 99%

“…The majority of studies (93%) utilized passively sensed data, by using smartphone sensors for data collection. While there were a few common in-built hardware sensors such as the accelerometer [ 3 , 11 , 53 ] and GPS/location sensor [ 9 , 60 , 69 ] used in most studies (62% and 53%, respectively), some also looked at software features such as application usage [ 61 , 91 ]. Studies used a range of sensors and software features to collect various types of contextual information.…”

Section: Resultsmentioning

confidence: 99%

“…Although the wide range of available sensors facilitate diverse data collection, the types of sensors used and the data-collection rate influence power consumption. A higher sampling rate provides larger amounts of data points, but also has a greater impact on battery life [ 3 , 53 , 101 ]. It is crucial to ensure that the power consumption of the sensing application is minimal, to ensure user acceptance [ 15 , 99 , 101 , 102 ].…”

Section: Resultsmentioning

confidence: 99%

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Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

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Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

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Application of machine learning to predict transport modes from GPS, accelerometer, and heart rate data

et al. 2022

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Background There has been an increased focus on active transport, but the measurement of active transport is still difficult and error-prone. Sensor data have been used to predict active transport. While heart rate data have very rarely been considered before, this study used random forests (RF) to predict transport modes using Global Positioning System (GPS), accelerometer, and heart rate data and paid attention to methodological issues related to the prediction strategy and post-processing. Methods The RECORD MultiSensor study collected GPS, accelerometer, and heart rate data over seven days from 126 participants living in the Ile-de-France region. RF models were built to predict transport modes for every minute (ground truth information on modes is from a GPS-based mobility survey), splitting observations between a Training dataset and a Test dataset at the participant level instead at the minute level. Moreover, several window sizes were tested for the post-processing moving average of the predicted transport mode. Results The minute-level prediction rate of being on trips vs. at a visited location was 90%. Final prediction rates of transport modes ranged from 65% for public transport to 95% for biking. Using minute-level observations from the same participants in the Training and Test sets (as RF spontaneously does) upwardly biases prediction rates. The inclusion of heart rate data improved prediction rates only for biking. A 3 to 5-min bandwidth moving average was optimum for a posteriori homogenization. Conclusion Heart rate only very slightly contributed to better predictions for specific transport modes. Moreover, our study shows that Training and Test sets must be carefully defined in RF models and that post-processing with carefully chosen moving average windows can improve predictions.

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MOBIUS: Smart Mobility Tracking with Smartphone Sensors

Cited by 2 publications

References 13 publications

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Application of machine learning to predict transport modes from GPS, accelerometer, and heart rate data

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