Wearable fitness trackers and smartphone pedometer apps: Their effect on transport mode choice in a transit-oriented city

Wong, R.C.P.; Yang, Linchuan; Szeto, W.Y.

doi:10.1016/j.tbs.2020.10.006

Cited by 9 publications

(7 citation statements)

References 54 publications

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“…According to research, walking is moderate in intensity and easy to achieve, making it one of the most common and beneficial physical activities for the elderly [5][6][7]. Additionally, walking has also become the common travel mode adopted by the elderly [8].…”

Section: Benefits Of Physical Activitymentioning

confidence: 99%

Differences in the Correlation between the Built Environment and Walking, Moderate, and Vigorous Physical Activity among the Elderly in Low- and High-Income Areas

Zang

Xian

Qiu

et al. 2022

IJERPH

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Studies have proved that activity and fitness behaviors are closely related to the quality of life and health status of the elderly. However, different intensities of physical activity (PA)—walking, moderate PA, and vigorous PA—have different correlations with the built environment (BE). This study combines the high and low socioeconomic status (SES) of Guangzhou to establish two types of BE models. The physical activity time of 600 elderly people was collected from questionnaires. Through ArcGIS software, 300 m, 500 m, 800 m, and 1000 m buffer zones were identified, and the land use diversity, street design, population density, distance to destination, distance to public transportation—the five Ds of the BE—were measured. SPSS software was adopted to analyze the correlation between the BE and PA. Results: The PA of people living in low-SES areas was more dependent on the BE, whereas the correlation may be limited in high SES areas. Moreover, in low SES areas, walking was negatively correlated with street connectivity; moderate PA was positively correlated with street connectivity and the shortest distance to the subway station, but negatively correlated with the density of entertainment points of interest (POIs). Studying the relevant factors of the environment can propose better strategies to improve the initiative of the elderly to engage in PA.

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Section: Benefits Of Physical Activitymentioning

confidence: 99%

Differences in the Correlation between the Built Environment and Walking, Moderate, and Vigorous Physical Activity among the Elderly in Low- and High-Income Areas

Zang

Xian

Qiu

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on a survey, Abdullah et al [22] concluded, that amongst other factors, gender, car ownership, employment status, travel distance, primary purpose of travelling are significant factors affecting mode choice before the pandemic. Smartphones and fitness tracking apps may also influence mode choice [23].…”

Section: Research On the Choice For Sustainable Modes Of Transportmentioning

confidence: 99%

Effective Speed: Factors That Influence the Attractiveness of Cost Effective and Sustainable Modes of Transport in Cities

Schnieder

2023

Sustainability

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Background: People invest hours of their working day to pay for their vehicle. Is this a worthwhile use of their time? Wouldn’t public transport be a more economical solution for those on low income? Which demographic group typically chooses the most effective mode of transport? Aim: The two studies presented in this paper answer those fundamental questions based on the effective speed concept. This is calculated by dividing the distance travelled by time (i.e., minutes spent travelling and earning the money to afford it). Methodology: A digital model of the residents in Berlin is created to calculate the ‘effective speed’ for everyone. In the second study, based on a survey of residents in New York City, the best mode of transport for each respondent based on their effective speed has been identified and compared to the mode chosen. Results: The results of the Berlin case study show that the mode share for cars would be less than 6% if people choose the mode of transport with the highest effective speed. A similar picture is seen in NYC. Factors that significantly affect whether people who should use public transport, do so, include: age, gender, education, place of work or home.

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“…The output layer is a dense layer with softmax as the activation function. The output data size is (1,4), so the model can classify four labels. When the LSTM model completes the identification, the oldest 32 samples are removed, and the latest 32 samples are added into the input data (total 128 samples) for LSTM.…”

Section: Posture Recognition Algorithmmentioning

confidence: 99%

“…The dimension of the input data is (1,128,6), and the IMU six-axis data are packaged in 32 time steps, as shown in Figure 5. Each set of data has 128 consecutive samples.…”

Section: Posture Recognition Algorithmmentioning

confidence: 99%

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Development of AI Algorithm for Weight Training Using Inertial Measurement Units

Lin

et al. 2022

Applied Sciences

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Thanks to the rapid development of Wearable Fitness Trackers (WFTs) and Smartphone Pedometer Apps (SPAs), people are keeping an eye on their health through fitness and heart rate tracking; therefore, home weight training exercises have received a lot of attention lately. A multi-procedure intelligent algorithm for weight training using two inertial measurement units (IMUs) is proposed in this paper. The first procedure is for motion tracking that estimates the arm orientation and calculates the positions of the wrist and elbow. The second procedure is for posture recognition based on deep learning, which identifies the type of exercise posture. The final procedure is for exercise prescription variables, which first infers the user’s exercise state based on the results of the previous two procedures, triggers the corresponding event, and calculates the key indicators of the weight training exercise (exercise prescription variables), including exercise items, repetitions, sets, training capacity, workout capacity, training period, explosive power, etc.). This study integrates the hardware and software as a complete system. The developed smartphone App is able to receive heart rate data, to analyze the user’s exercise state, and to calculate the exercise prescription variables automatically in real-time. The dashboard in the user interface of the smartphone App can display exercise information through Unity’s Animation System (avatar) and graphics, and records are stored by the SQLite database. The designed system was proven by two types of experimental verification tests. The first type is to control a stepper motor to rotate the designed IMU and to compare the rotation angle obtained from the IMU with the rotation angle of the controlled stepper motor. The average mean absolute error of estimation for 31 repeated experiments is 1.485 degrees. The second type is to use Mediapipe Pose to calculate the position of the wrist and the angles of upper arm and forearm between the Z-axis, and these calculated data are compared with the designed system. The root-mean-square (RMS) error of positions of the wrist is 2.43 cm, and the RMS errors of two angles are 5.654 and 4.385 degrees for upper arm and forearm, respectively. For posture recognition, 12 participants were divided into training group and test group. Eighty percent and 20% of 24,963 samples of 10 participants were used for the training and validation of the LSTM model, respectively. Three-thousand-three-hundred-and-fifty-nine samples of two participants were used to evaluate the performance of the trained LSTM model. The accuracy reached 99%, and F1 score was 0.99. When compared with the other LSTM-based variants, the accuracy of one-layer LSTM presented in this paper is still promising. The exercise prescription variables provided by the presented system are helpful for weight trainers/trainees to closely keep an eye on their fitness progress and for improving their health.

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Wearable fitness trackers and smartphone pedometer apps: Their effect on transport mode choice in a transit-oriented city

Cited by 9 publications

References 54 publications

Differences in the Correlation between the Built Environment and Walking, Moderate, and Vigorous Physical Activity among the Elderly in Low- and High-Income Areas

Differences in the Correlation between the Built Environment and Walking, Moderate, and Vigorous Physical Activity among the Elderly in Low- and High-Income Areas

Effective Speed: Factors That Influence the Attractiveness of Cost Effective and Sustainable Modes of Transport in Cities

Development of AI Algorithm for Weight Training Using Inertial Measurement Units

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