A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers
Abstract
:1. Introduction
2. Description of the Wearable System
2.1. Flexible Sensors Based on FBG
2.2. Sensors Positioning and Measurement Parameters
3. System Assessment
3.1. Experimental Set-Up and Protocol
3.2. Data Analysis
3.2.1. Neck Movements
- the outputs of both the wearable and the MoCap systems were normalized in amplitude and plotted over time to evaluate trend similarity between signals;
- the FE and AR repetitions were detected by using a custom peak detection algorithm in MATLAB environment. FE movements were detected by considering the maximum peaks of both MoCap and FBG1 signals: when increases during the neck flexion (signal provided by the MoCap) FBG1 is strained with a consequent increase of λB (Figure 5A,B). Right AR movements were detected by considering the minimum peaks of both MoCap and FBG2 signals: when θAR decreases during the right AR (signal provided by the MoCap) FBG2 is compressed with a consequent decrement of λB (Figure 5C). These data were collected during the first 5 AR repetitions; left AR movements were detected by considering the maximum peaks of both MoCap and FBG2 signals because when θAR increases during the left AR (signal provided by the MoCap) FBG2 is strained with a consequent increment of λB (Figure 5B). These data were collected during the last 5 AR repetitions.
3.2.2. Breathing Activity
- The outputs of the wearable system and the flowmeter were normalized in amplitude and split into quiet breathing-related signals and tachypnea-related ones (i.e., FBG1qb, FBG2qb, FLOWqb, FBG1tc, FBG2tc, and FLOWtc), as shown in Figure 6;
- The signal of both FBG1qb and FBG1tc were inverted since the FBG1 was compressed during the inspiration (when the volume of lungs increases) and tensioned during the expiration (when the volume of lungs decreases). This step was not implemented on the FBG2 output since its trend in time matches that of the reference system;
- a third-order Butterworth low pass filter was applied on signals collected during quiet breathing (cut-off frequency, fc, of 0.5 Hz) and during tachypnea (fc of 3 Hz);
- spectral analysis in terms of power spectral density (PSD) was performed on the filtered signals and the maximum frequency (f0) of both the reference and the wearable systems signals were evaluated (Figure 7);
- peak detection was performed by using findpeaks in MATLAB environment: the input parameter related to minimum peaks distance was set starting from the value of f0 (Figure 7);
- the respiratory periods of each breath (i.e., TRi) was computed as the time elapsed between two consecutive maximum peaks of the signal provided by FBG1, FBG2, and the flowmeter, see Figure 7. The fRi values during both quiet breathing and tachypnea were estimated as 60/TRi and expressed as breaths per minute (bpm).
- in terms of percentage error () as in:
- in terms of absolute percentage errors for a breath-by-breath analysis:
- by calculating the mean value of the breath-by-breath absolute percentage errors (i.e., MAPE), for each volunteer as in:
3.3. Results
3.3.1. Detection of Neck Movements
3.3.2. Breathing Activity: Respiratory Frequency Estimation
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Age (Years) | Height (cm) | Body Mass (kg) | Neck Circumference (cm) | |
---|---|---|---|---|
Volunteer 1 | 23 | 183 | 125 | 46 |
Volunteer 2 | 28 | 171 | 61 | 32 |
Volunteer 3 | 27 | 178 | 85 | 38 |
Volunteer 4 | 31 | 163 | 60 | 38 |
Volunteer 5 | 39 | 171 | 71 | 43 |
Quiet Breathing | |||
Volunteer | FBG1 [bpm] | FLOW [bpm] | [%] |
1 | - | 15.37 | - |
2 | 14.47 | 14.63 | −1.09 |
3 | 14.36 | 14.14 | 1.53 |
4 | 22.36 | 22.09 | 1.22 |
5 | 15.15 | 15.22 | −0.45 |
FBG2 [bpm] | FLOW [bpm] | [%] | |
1 | 15.30 | 15.37 | −0.48 |
2 | 14.65 | 14.63 | 0.15 |
3 | 15.00 | 14.14 | 6.09 |
4 | 22.40 | 22.09 | 1.40 |
5 | 15.10 | 15.22 | −0.79 |
Tachypnea | |||
Volunteer | FBG1 [bpm] | FLOW [bpm] | [%] |
1 | - | 83.62 | - |
2 | 48.65 | 48.80 | −0.32 |
3 | 83.28 | 83.87 | −0.71 |
4 | 54.53 | 54.58 | −0.09 |
5 | 37.88 | 38.09 | −0.56 |
FBG2 [bpm] | FLOW [bpm] | [%] | |
1 | 82.02 | 83.62 | −1.90 |
2 | 48.46 | 48.80 | −0.72 |
3 | 82.98 | 83.87 | −1.05 |
4 | 54.99 | 54.58 | 0.75 |
5 | 38.01 | 38.09 | −0.19 |
Quiet Breathing | ||||||||||
Volunteer | |epFBG1| [%] | MAPEFBG1 [%] | ||||||||
1 | - | - | - | - | - | - | - | - | - | - |
2 | 10.40 | 0.34 | 0.28 | 0.19 | 0.58 | 1.91 | 2.24 | 2.93 | 3.22 | 2.45 |
3 | 16.66 | 10.88 | 2.28 | 4.97 | 10.26 | 7.75 | 13.7 | 5.62 | 8.17 | 8.92 |
4 | 12.32 | 7.11 | 14.04 | 13.51 | 10.17 | 28.65 | 1.24 | 15.56 | 13.21 | 12.87 |
5 | 0.21 | 8.24 | 4.48 | 5.92 | 0.91 | 3.56 | 6.58 | 4.71 | 1.04 | 1.47 |
|epFBG2| [%] | MAPEFBG2 [%] | |||||||||
1 | 5.28 | 3.76 | 0.20 | 2.18 | 4.75 | 4.19 | 1.33 | 4.92 | 4.33 | 3.44 |
2 | 2.69 | 0.86 | 2.06 | 1.14 | 0.97 | 2.09 | 0.19 | 6.76 | 4.42 | 2.36 |
3 | 29.36 | 12.12 | 4.05 | 3.63 | 17.35 | 27.82 | 8.21 | 15.75 | 19.93 | 15.36 |
4 | 15.34 | 16.03 | 4.22 | 0.85 | 7.36 | 3.46 | 3.33 | 0.16 | 1.35 | 5.79 |
5 | 2.53 | 1.52 | 0.95 | 1.95 | ~0 | 0.41 | 2.31 | 2.89 | 1.23 | 1.53 |
Tachypnea | ||||||||||
Volunteer | |epFBG1| [%] | MAPEFBG1 [%] | ||||||||
1 | - | - | - | - | - | - | - | - | - | - |
2 | 3.74 | 0.65 | 3.11 | 3.34 | 7.56 | 6.21 | 4.64 | 11.54 | 3.75 | 4.95 |
3 | 1.69 | 2.70 | 1.71 | 0.55 | 3.72 | 4.09 | ~0 | 5.82 | 1.16 | 2.38 |
4 | 2.95 | 9.77 | 4.48 | 18.86 | 0.37 | 1.46 | 1.44 | 5.35 | 8.10 | 5.86 |
5 | 0.52 | 1.06 | 0.77 | 1.84 | 0.50 | 0.73 | 0.24 | 1.31 | 2.11 | 1.01 |
|epFBG2| [%] | MAPEFBG2 [%] | |||||||||
1 | 6.95 | 2.26 | 0.56 | 2.72 | 2.63 | 3.68 | 1.63 | 2.81 | 5.13 | 3.15 |
2 | 4.07 | 0.97 | 3.67 | 2.52 | 3.34 | 0.96 | 0.97 | 6.69 | 3.45 | 2.96 |
3 | 8.85 | 7.14 | 0.55 | ~0 | 1.12 | 1.14 | 0.55 | 1.11 | 7.41 | 3.10 |
4 | 6.41 | 2.47 | 9.92 | 5.24 | 5.43 | 2.11 | 6.23 | 6.29 | ~0 | 4.90 |
5 | 0.52 | 1.33 | 0.26 | 1.84 | 0.49 | ~0 | 1.72 | 1.02 | 0.72 | 0.88 |
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Lo Presti, D.; Carnevale, A.; D’Abbraccio, J.; Massari, L.; Massaroni, C.; Sabbadini, R.; Zaltieri, M.; Di Tocco, J.; Bravi, M.; Miccinilli, S.; et al. A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers. Sensors 2020, 20, 536. https://doi.org/10.3390/s20020536
Lo Presti D, Carnevale A, D’Abbraccio J, Massari L, Massaroni C, Sabbadini R, Zaltieri M, Di Tocco J, Bravi M, Miccinilli S, et al. A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers. Sensors. 2020; 20(2):536. https://doi.org/10.3390/s20020536
Chicago/Turabian StyleLo Presti, Daniela, Arianna Carnevale, Jessica D’Abbraccio, Luca Massari, Carlo Massaroni, Riccardo Sabbadini, Martina Zaltieri, Joshua Di Tocco, Marco Bravi, Sandra Miccinilli, and et al. 2020. "A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers" Sensors 20, no. 2: 536. https://doi.org/10.3390/s20020536
APA StyleLo Presti, D., Carnevale, A., D’Abbraccio, J., Massari, L., Massaroni, C., Sabbadini, R., Zaltieri, M., Di Tocco, J., Bravi, M., Miccinilli, S., Sterzi, S., Longo, U. G., Denaro, V., Caponero, M. A., Formica, D., Oddo, C. M., & Schena, E. (2020). A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers. Sensors, 20(2), 536. https://doi.org/10.3390/s20020536