# Towards Model-Based Online Monitoring of Cyclist’s Head Thermal Comfort: Smart Helmet Concept and Prototype

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## Abstract

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## Featured Application

**In this work, we introduce the basis for a personalised adaptive model to predict head thermal comfort using streaming data of easily measured variables, which can be used for real-time monitoring of a cyclist’s thermal comfort and adaptive controlling of smart wearable applications.**

## Abstract

## 1. Introduction

- (i)
- identifying a general model to estimate thermal comfort based on a few variables, the measurements of which can be integrated in helmets;
- (ii)
- developing and testing a prototype of a smart helmet based on the identified general thermal comfort model; and
- (iii)
- introducing the framework of calculation for an adaptive personalised reduced-order model to predict a cyclist’s under-helmet thermal comfort using nonintrusive, easily measured variables.

## 2. Materials and Methods

#### 2.1. Development of General Thermal Comfort Predictive Model

#### 2.1.1. Experimental Setup and Test Subjects

^{3}·h

^{−1}. A 50 cm long honeycomb gauze structure, placed 25 cm from the fans, was used to obtain a quasi-laminar flow within the open-loop wind tunnel (for more information about the wind tunnel, see [18]). The air speed near the test subject’s head was set to 2.5 m·s

^{−1}to simulate recreational cycling for adults and children. The wind tunnel was placed inside a climate-controlled chamber (Figure 2), the inner dimensions of which were 4 × 11 × 5 m ($w$×$\text{}l$×$\text{}h$). The air temperature within the climate chamber was controllable within the range of 15–35 °C. Additionally, the ventilation rate within the climate chamber was controllable within the range of 0–2700 m

^{3}·h

^{−1}(i.e., 0–11.25 volume refreshments per hour).

#### 2.1.2. Pretest Experiments

#### 2.1.3. Thermal Comfort and Variable Screening Experimental Protocol

^{TM}bioharness Bt) in combination with a built-in optical heart rate sensor (PPG, Lifebeam) in the bicycle helmet (Lazer Z1 and Lazer Z1 fast = Lazer Z1 + aeroshell). The temperatures of the subject’s forehead, neck, inside of the ear and the air under the bicycle helmet (at front, back, right and left) were continuously measured using calibrated thermocouples (type-T) with a sampling frequency of 1 Hz.

_{h}, which can be mathematically expressed as follows:

_{h}, so that analysis of a dynamic response due to the bicycle helmet was possible. With the help of the JMP Pro

^{®}software, different combinations (referred to as runs) of the input variables were generated. In general, each participant (test subject) was subjected to four runs (combinations) of the generated ones. Table 3 shows the experimental design for test subjects ($j$) 1 and 8 as an example, where each time slot corresponds to one run (a combination of the four input variables).

#### 2.1.4. General Linear Regression (LR) Model Identification and Offline Parameter Estimation

#### 2.2. Development of Smart Helmet Prototype

#### 2.3. Testing the Developed Smart Helmet Prototype

#### 2.3.1. Test Subjects

^{−2}; and body surface area—1.9 (±0.1) m

^{2}. Prior to the trial, a signed written consent form was obtained from all participants after a detailed description of the protocol, discomforts and benefits. The experimental protocol was approved by the ethical review board at the University of Thessaly, School of Exercise Science in accordance with the recommendations of the Declaration of Helsinki.

#### 2.3.2. Experimental Design and Protocol

^{−1}was provided with a large 80 cm diameter industrial fan positioned in front of the participant at a distance of 140 cm from the bicycle saddle. All participants were instructed to abstain from vigorous physical activity 24 h prior the experimental trial and consume at least 500 mL of water and a light meal 2 h before arrival at the laboratory.

## 3. Results

#### 3.1. Pretest Experiments

#### 3.2. Development of Offline (General) Thermal Comfort Model

^{−1}), the helmet wearing level (0 = no helmet, 0.5 = helmet and 1 = helmet + aeroshell) and the applied mechanical work rate (power) level ($P$, W). The measured variables related to the bioresponses of the test subject (right graphs) were heart rate (${H}_{R}$, bpm), the temperature difference ($\u2206T$, °C) between the average temperature beneath the helmet and the ambient air temperature, the temperature difference ($\u2206{T}_{ear}$, °C) between the ear temperature and the ambient air temperature and the thermal comfort (red line) and sensation (blue line) scores.

^{®}profiler tool [28], as visualised in Figure 8. For convenience of this analysis, the values of each input variable were scaled (normalised) in such a way to lie in the closed interval [−1, +1], where -1 indicates the variable’s low level and +1 indicates its high level (Figure 8). The scaling of each variable value $i\left(k\right)$ was done according to the following formula:

_{R}) variable included in the compact model (3) directly linked to the applied mechanical work rate ($P$), hence the effect of $P$, included in model (2), translated by the bioresponse represented by H

_{R}(e.g., [29]) included in model (3). According to Newton’s law of cooling, temperature difference ($\u2206T$) is the driving force for the convective heat transfer (${Q}_{h}$) between the cyclist’s head and the ambient air. The heat flux ($q$) is proportional to $\u2206T$ and the convective heat transfer coefficient (${h}_{c}$) links both variables as follows:

^{2}∙°C) is a combination of the heat transfer coefficient of the air (${h}_{air}$) and that of the helmet (${h}_{H}=\frac{1}{{R}_{h}}$); hence,

_{R}) of the cyclist and the interaction variable $[{T}_{a}{R}_{h}]$ between ambient temperature (${T}_{a}$) and helmet thermal resistance (${R}_{h}$), were suitable enough to estimate the cyclist’s thermal comfort (${T}_{C}$) under the bicycle helmet. These selected variables were the basis for developing a reduced-order personalised model for real-time monitoring of a cyclist’s thermal comfort under the helmet. Additionally, from a practical point of view, these three variables were suitable to be measured using integrated sensors in the cyclist’s helmet, as is shown in the following subsection.

#### 3.3. Testing the SmartHelmet Prototype and Validation of the Developed General Model

_{R}) obtained during the TT from all seven test subjects using the developed prototype smart helmet.

#### 3.4. Introduction of Online Personalisation and Adaptive Modelling Algorithm

#### 3.4.1. Offline Linear Regression Model

#### 3.4.2. Streaming Data

#### 3.4.3. Online Parameter Estimation Algorithm

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Schematic diagram showing the general framework of the development of a personalised adaptive model for the smart helmet prototype.

**Figure 2.**Schematic representation showing the used bicycle fixed inside a customised wind tunnel and placed within a climate chamber (

**left**) and a photograph of a test subject riding the bike within the wind tunnel (

**right**).

**Figure 3.**The developed smart helmet prototype showing the microcontroller and the humidity and temperature sensor on the back side of the helmet (

**left picture**) and the four NTC temperature sensors placed in the inner body of the helmet (

**right picture**).

**Figure 6.**Obtained power values of the pretest. These power values correspond to the power value when they exceeded a respiratory exchange ratio (RER) of one.

**Figure 8.**Visualisation of the model prediction traces showing the interaction effect of the thermal resistance (${R}_{h}$) and ambient temperature (${T}_{a}$) on the predicted thermal comfort. (

**a**) When the temperature was low (20 °C), additional thermal resistance was perceived as comfortable. However, (

**b**) when the temperature was high (30 °C), additional thermal resistance was perceived as uncomfortable. The values of the input variables were normalised in the range between -1 and 1, which correspond to low and high levels, respectively. Table 6 shows the average parameter estimates of the developed compact regression model (3) for the 15 test subjects.

**Figure 9.**Average values of ratings of perceived exertion (RPE), thermal comfort (${T}_{C}$) and thermal sensation (${T}_{S}$) between the start (PRE) and end (POST) times of the TT (* indicates a significant difference of p < 0.05).

**Figure 10.**

**(a**) Average temperature (${\overline{T}}_{h}$) beneath the helmet, (

**b**) average temperature difference ($\Delta T$) between the average temperature and the ambient air temperature and (

**c**) average heart rate (H

_{R}) obtained during the TT from all test subjects.

**Figure 11.**Schematic representation of the proposed online personalisation algorithm to predict thermal comfort under the helmet. The retuning and personalisation algorithm is based on data streaming obtained from the developed SmartHelmet prototype and the cyclist’s personal vote of thermal comfort acquired from the developed SmartHelmet App. The streamed data is fed, together with the developed offline model, to an online parameter estimation algorithm based on a recursive least-squares (RLS) algorithm.

${\mathit{T}}_{\mathit{a}}$ (°C) | $\mathit{v}$ (m·s^{−1}) | $\mathit{P}$ (W) | ${\mathit{R}}_{\mathit{h}}$(m^{2}·°C·W^{−1}) | |
---|---|---|---|---|

Low level | 20 | 0 | 50% (PRER = 1) | 0 (no helmet) |

Midlevel | / | / | / | 0.045 (with helmet) |

High level | 30 | 4 | 90% (PRER = 1) | 0.060 (helmet + aeroshell) |

**Table 2.**Thermal comfort scale introduced by Gagge et al. [10], excluding the cold sensation votes.

Scale | Thermal Comfort Perception |
---|---|

1 | Comfortable |

2 | Slightly uncomfortable |

3 | Uncomfortable |

4 | Very uncomfortable |

**Table 3.**Experimental design for test subjects 1 and 8, showing the four runs (combinations) of input variables with three different levels, namely, high (

**↑**), mid (−) and low (

**↓**).

Participant (No. = j) | Variables | Timeslot (1) | Timeslot (2) | Timeslot (3) | Timeslot (4) |
---|---|---|---|---|---|

$\mathit{j}$ = 1 | ${T}_{a}$ (°C) | ↓ | ↓ | ↓ | ↓ |

$v$ (m·s^{−1}) | ↑ | ↓ | ↑ | ↓ | |

$P$ (% PPER = 1) | ↓ | ↓ | ↑ | ↑ | |

${R}_{h}$ (m^{2}·°C·W^{−1}) | − | ↑ | − | ↓ | |

$\mathit{j}$ = 8 | ${T}_{a}$ (°C) | ↑ | ↑ | ↑ | ↑ |

$v$ (m·s^{−1}) | ↑ | ↑ | ↓ | ↓ | |

$P$ (% PPER = 1) | ↑ | ↓ | ↑ | ↑ | |

${R}_{h}$ (m^{2}·°C·W^{−1}) | − | − | ↓ | ↑ |

**Table 4.**The mean average power output, pedalling cadence and 30 km time-trial (TT) obtained from all test subjects.

Variable | Average (±Standard Deviation) |
---|---|

Power output (W) | 176.5 (±24.2) |

Cadence (rpm) | 93.7 (±14.2) |

30 km TT duration (min) | 56.9 (±7.9) |

**Table 5.**The estimation results of the selected linear regression model (3) to predict thermal comfort, showing the average model estimates for the 15 test subjects.

Term | Parameter | Estimate | Std. Error | t-Ratio | P > |t| |
---|---|---|---|---|---|

intercept | $\alpha $ | 2.36 | 0.14 | 16.80 | <0.0001 * |

${T}_{a}$ | ${\beta}_{1}$ | −0.40 | 0.11 | −3.52 | 0.0025 * |

$v$ | ${\beta}_{2}$ | −0.36 | 0.07 | −4.85 | <0.0001 * |

$P$ | ${\beta}_{3}$ | 0.41 | 0.07 | 5.45 | <0.0001 * |

$[{T}_{a}{R}_{h}]$ | ${\beta}_{4}$ | 0.25 | 0.01 | 2.52 | 0.015 * |

**Table 6.**The estimation results of the compact regression model (3) to predict thermal comfort, showing the average model estimates for the 15 test subjects.

Term | Parameter | Estimate | Std. Error | t-Ratio | P > |t| |
---|---|---|---|---|---|

intercept | $\alpha $ | 1.86 | 0.21 | 13.61 | <0.0001 * |

$\u2206T$ | ${\beta}_{1}$ | 1.30 | 0.19 | 5.22 | 0.0031 * |

${H}_{R}$ | ${\beta}_{2}$ | −0.62 | 0.13 | −5.67 | <0.0014 * |

$[{T}_{a}{R}_{h}]$ | ${\beta}_{3}$ | 0.35 | 0.07 | 2.52 | 0.0140 * |

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## Share and Cite

**MDPI and ACS Style**

Youssef, A.; Colon, J.; Mantzios, K.; Gkiata, P.; Mayor, T.S.; Flouris, A.D.; De Bruyne, G.; Aerts, J.-M. Towards Model-Based Online Monitoring of Cyclist’s Head Thermal Comfort: Smart Helmet Concept and Prototype. *Appl. Sci.* **2019**, *9*, 3170.
https://doi.org/10.3390/app9153170

**AMA Style**

Youssef A, Colon J, Mantzios K, Gkiata P, Mayor TS, Flouris AD, De Bruyne G, Aerts J-M. Towards Model-Based Online Monitoring of Cyclist’s Head Thermal Comfort: Smart Helmet Concept and Prototype. *Applied Sciences*. 2019; 9(15):3170.
https://doi.org/10.3390/app9153170

**Chicago/Turabian Style**

Youssef, Ali, Jeroen Colon, Konstantinos Mantzios, Paraskevi Gkiata, Tiago S. Mayor, Andreas D. Flouris, Guido De Bruyne, and Jean-Marie Aerts. 2019. "Towards Model-Based Online Monitoring of Cyclist’s Head Thermal Comfort: Smart Helmet Concept and Prototype" *Applied Sciences* 9, no. 15: 3170.
https://doi.org/10.3390/app9153170