Personalized Tracking of Physical Activity in Children Using a Wearable Heart Rate Monitor
Abstract
:1. Introduction
2. Materials and Methods
2.1. Adaptive Systems
2.2. Adaptation Model and Methodology
2.2.1. Step 1: Define Application Outline
2.2.2. Step 2: Define Personalization
- Transitions Game Scenario Flow (C1): Changing the order in which scenarios are executed by the game based on the user’s average HR history and calorie expenditure so as to ensure the less physically demanding scenarios for each user get executed prior to the more demanding scenarios.
- Elongated Adjustable Body Motions (C2): The movement of the body is variable, depending on the characteristics of the user. The game autonomously decides if more elongation or contraction of the body parts is required to achieve the goal of the game scenario.
- Free Choice (C3): Freedom to choose the desired scenario to play, subject to personalized recommendations. This means that depending on the characteristics of the user or the levels previously accomplished in the game different scenarios can be enabled or disabled for the user.
- Overall Operations of the User Interface (C4): Modification of options displayed by the user interface (UI) backed by the user’s preferences. This refers to colors, avatar, or general settings such as sound, among other configurations.
- Execution Based on Physiological Measures (C5): Adjusts the exergame runtime parameters to the user’s physiological measures.
2.2.3. Step 3: Define Customization Questions
- C1. What are the user’s current PA measurements in order to go to the next game scenario? (Q1)
- C2. How accurate are the user’s body movements for the current scenario? (Q2).
- C3. Which are the best-ranked scenarios for a particular user? (Q3)?
- C4. What is the user experience reported for any given UI? (Q4).
- C5. What is the user’s physiological response during gameplay? (Q5).
2.2.4. Step 4: Describe User Properties
2.2.5. Step 5: Describe Events
2.2.6. Step 6: Pruning
2.2.7. Step 7: Describe the Dynamic Behavior
d = d + 1
} else if (d > deltaD and avgHR < 95) {
d = 0
deltaD = deltaD + deltaD × w
vh = vh – deltaD × w
} else {
d = 0
deltaD = deltaD – deltaD × w
vh = vh − vh × w
}
points = points + 1
countdown = countdown − 1; countdown ≥ 5
} else if (bpressed and countdown > 0 and avgHR > 95){
points = points + 1
countdown = countdown + 1
} else if (countdown = 0) {
points = points − 1
countdown = countdown + 1; countdown ≤ 15
}
points = points + 1
Sgenerate = Sgenerate + 0.1; Sgenerate > 0
}else if (Spressed and Sonscreen and avgHR > 95){
points = points + 1
Sgenerate = Sgenerate − 0.1; Sgenerate < 5 × 0.366
}else if (!Sonscreen){
points = points − 1
}
2.2.8. Step 8: Evaluation
2.3. Wearable Technology Selection
2.4. Implementation Architecture
2.5. Experimental Evaluation
2.5.1. Pilot Study
2.5.2. Field Test Study
3. Results
3.1. Pilot Study Results
3.1.1. Descriptive Analysis
3.1.2. Inferential Analysis
3.2. Field Test Results
3.2.1. Descriptive Analysis
3.2.2. Inferential Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Statistics | HrateP1CIS | HrateP2CIS | HrateP3CIS | HrateP1UAS | HrateP2UAS | HrateP3UAS | Gcalorico1CIS | Gcalorico2CIS | Gcalorico3CIS | Gcalorico1UAS | Gcalorico2UAS | Gcalorico3UAS |
---|---|---|---|---|---|---|---|---|---|---|---|---|
N | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
Mean | 72.31 | 72.06 | 66.56 | 88.41 | 78.66 | 88.59 | 5.67 | 7.67 | 4.33 | 10.33 | 9.33 | 10.67 |
Std. Dev | 3.05 | 3.74 | 2.77 | 4.68 | 2.34 | 9.07 | 2.89 | 2.52 | 1.53 | 3.51 | 3.06 | 1.53 |
Variance | 9.31 | 13.98 | 7.65 | 21.94 | 5.46 | 82.21 | 8.33 | 6.33 | 2.33 | 12.33 | 9.33 | 2.33 |
Asymmetry | −0.74 | −1.73 | −1.12 | 1.63 | −0.38 | −0.20 | 1.73 | −0.59 | 0.94 | 0.42 | −0.94 | −0.94 |
Interval | 6.04 | 6.56 | 5.38 | 8.60 | 4.66 | 1812 | 5.00 | 5.00 | 3.00 | 7.00 | 6.00 | 3.00 |
Dependent Variables | Differences Paired t-Test | |||||||
---|---|---|---|---|---|---|---|---|
Average | Std. Dev | Av. Sta. Err | Lower | Higher | t | df | Sign | |
HrateP1UAS-HrateP1CIS | 16.09 | 4.88 | 2.82 | 3.98 | 28.21 | 5.72 | 2 | 0.029 |
HrateP2UAS-HrateP2CIS | 6.60 | 1.88 | 1.08 | 1.94 | 11.27 | 6.09 | 2 | 0.026 |
HrateP3UAS-HrateP3CIS | 22.03 | 8.80 | 5.08 | 0.17 | 43.88 | 4.34 | 2 | 0.049 |
Gcalorico1UAS-Gcalorico1CIS | 4.67 | 1.53 | 0.88 | 0.87 | 8.46 | 5.29 | 2 | 0.034 |
Gcalorico2UAS-Gcalorico2CIS | 1.67 | 0.58 | 0.33 | 0.23 | 3.10 | 5.00 | 2 | 0.038 |
Gcalorico3UAS-Gcalorico3CIS | 6.33 | 2.31 | 1.33 | 0.60 | 12.07 | 4.75 | 2 | 0.042 |
Statistics | HrateP1CIS | HrateP2CIS | HrateP3CIS | HrateP1UAS | HrateP2UAS | HrateP3UAS | Gcalorico1CIS | Gcalorico2CIS | Gcalorico3CIS | Gcalorico1UAS | Gcalorico2UAS | Gcalorico3UAS |
---|---|---|---|---|---|---|---|---|---|---|---|---|
N | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Mean | 76.44 | 78.94 | 79.78 | 78.95 | 83.83 | 80.73 | 7.41 | 10.28 | 8.03 | 9.90 | 12.28 | 9.76 |
Std. Dev | 10.39 | 9.43 | 13.70 | 6.92 | 9.55 | 11.61 | 3.26 | 4.84 | 3.77 | 5.39 | 6.09 | 6.03 |
Variance | 107.95 | 89.85 | 187.81 | 46.56 | 91.13 | 134.72 | 10.61 | 23.42 | 14.25 | 29.02 | 37.06 | 36.33 |
Kurtosis | 1.25 | 0.55 | 2.04 | −0.63 | 1.86 | 7.67 | 1.17 | −0.24 | −0.21 | 4.70 | 7.52 | 3.83 |
K. Stad. Err. | 0.85 | 0.85 | 0.85 | 0.85 | 0.85 | 0.85 | 0.85 | 0.85 | 0.85 | 0.85 | 0.85 | 0.85 |
Asymmetry | 1.41 | 1.11 | 1.45 | 0.33 | 1.31 | 2.54 | 1.13 | 0.67 | 0.89 | 1.41 | 2.35 | 1.73 |
Interval | 38.91 | 35.90 | 58.47 | 24.78 | 40.97 | 55.93 | 13.00 | 17.00 | 13.00 | 28.00 | 30.00 | 28.00 |
Dependent Variables | Differences Paired t-Test | |||||||
---|---|---|---|---|---|---|---|---|
Average | Std. Dev | Av. Sta. Err | Lower | Higher | t | df | Sign | |
HrateP1UAS-HrateP1CIS | 2.51 | 13.25 | 2.46 | −2.53 | 7.55 | 1.02 | 28 | 0.316 |
HrateP2UAS-HrateP2CIS | 4.89 | 10.51 | 1.95 | 0.90 | 8.89 | 2.51 | 28 | 0.018 |
HrateP3UAS-HrateP3CIS | 0.96 | 19.19 | 3.56 | −6.34 | 8.25 | 0.27 | 28 | 0.790 |
Gcalorico1UAS-Gcalorico1CIS | 2.48 | 6.12 | 1.14 | 0.15 | 4.81 | 2.18 | 28 | 0.037 |
Gcalorico2UAS-Gcalorico2CIS | 2.00 | 6.25 | 1.16 | −0.38 | 4.38 | 1.72 | 28 | 0.096 |
Gcalorico3UAS-Gcalorico3CIS | 1.72 | 6.28 | 1.17 | −0.67 | 4.11 | 1.48 | 28 | 0.151 |
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Pérez, S.A.; Díaz, A.M.; López, D.M. Personalized Tracking of Physical Activity in Children Using a Wearable Heart Rate Monitor. Int. J. Environ. Res. Public Health 2020, 17, 5895. https://doi.org/10.3390/ijerph17165895
Pérez SA, Díaz AM, López DM. Personalized Tracking of Physical Activity in Children Using a Wearable Heart Rate Monitor. International Journal of Environmental Research and Public Health. 2020; 17(16):5895. https://doi.org/10.3390/ijerph17165895
Chicago/Turabian StylePérez, Santiago A., Ana M. Díaz, and Diego M. López. 2020. "Personalized Tracking of Physical Activity in Children Using a Wearable Heart Rate Monitor" International Journal of Environmental Research and Public Health 17, no. 16: 5895. https://doi.org/10.3390/ijerph17165895
APA StylePérez, S. A., Díaz, A. M., & López, D. M. (2020). Personalized Tracking of Physical Activity in Children Using a Wearable Heart Rate Monitor. International Journal of Environmental Research and Public Health, 17(16), 5895. https://doi.org/10.3390/ijerph17165895