Preliminary Evaluation of New Wearable Sensors to Study Incongruous Postures Held by Employees in Viticulture
Abstract
:1. Introduction
- Inertial Measurement Units (IMUs).These sensors, consisting of accelerometers, gyroscopes, and magnetometers, are utilized to perform quantitative biomechanical risk assessments. They are often employed to analyze work activities and to identify risks associated with upper limb movements and postures. The ZurichMOVE 1 is an example of a commercial IMU, widely used in gait monitoring. This sensor facilitates the collection of data such as axial accelerations and angular velocities, which are key biomechanical assessment parameters for activities such as walking or running.
- Surface Electromyography (sEMG).Employed to monitor muscle activity, sEMG sensors are often used to assess muscular fatigue and biomechanical loads during work tasks. These sensors provide direct data about muscle activity, facilitating the classification of work activities into low-risk and high-risk categories. Delsys Trigno is a well-known commercial sEMG that is widely used in the research for assessing muscle activity. It is acknowledged for being wireless and for providing a high signal quality, thus being suitable for both laboratory and field studies.
- Pressure and Feedback Sensors.Integrated with human augmentation technologies, these sensors enhance workers’ awareness of potential risks and improve safety and effectiveness within manual handling tasks. The Novel Pedar system is a pressure measurement system typically used in footwear to assess pressure distribution and loading patterns during activities. It is useful both in clinical settings and in research to evaluate biomechanical loads on the lower limbs.
- Cheap.Agricultural tasks are often difficult to standardize as they are influenced by variables such as weather conditions and plant layout on the vineyard, or characteristics of the training system. In order to acquire useful data, it is mandatory to monitor a large group of workers who, in the case of a pruning team, could number up to 10 or 12 workers [35]. For all of these reasons, wearable sensors for ergonomic analysis that focus on agricultural and viticultural sectors should be cost-effective, in order to allow the real-time monitoring of several operators.
- Robust.Agricultural instruments must withstand external conditions, such as rain and mud, as well as accidental impacts. For these reasons, wearable sensors for ergonomic analysis that focus on the agricultural and viticultural sectors should be robust in order to ensure their durability under adverse conditions and their functionality in case of accidental collisions.
- Adaptable and Easy to use.Farmers have different anthropometric characteristics and are generally involved in seasonal activities that are required to be completed in a short time. Therefore, wearable sensors, used to perform ergonomic analysis in a large group of farmers in a real scenario, should be easily adaptable to many anthropometric characteristics, as well as easy to use in order to keep up with agricultural activities. Furthermore, they should not need extensive further data processing, so as to give rapid feedback about injury risk assessments. Even if some emerging technologies, such as AI and machine learning, could facilitate data processing to assess biomechanical load, also within agriculture and during harvesting phases, their use does not seem to be easily applicable to the viticultural sector where the operator’s hand could be obscured by vegetation, making such technology less useful [36].
2. Materials and Methods
- the development and testing of a new wearable sensor (or measurement system);
- the application of the measurement system developed in a real case (i.e., the pruning of a vine).
2.1. Phase 1: Development and Testing of the Measuring System
2.1.1. Development of the Wearable Sensor
2.1.2. Processing of Raw Data Collected by a Sensor
- is the acceleration measured by the sensor,
- is the acceleration of the device excluding gravity,
- is the acceleration of gravity.
- is the velocity of the sensor at time t,
- is the velocity of the sensor at the previous time,
- is the acceleration corrected for orientation and free from the gravity effect.
- is the position of the sensor at time t,
- is the position of the sensor at the previous time,
- is the sensor at time t.
- is the orientation of the sensor at time t,
- is the orientation of the sensor at the previous time,
- is the angular velocity measured by gyroscopes,
- is the time interval between measurements.
2.1.3. Positioning of Sensors to Collect Movements of the Hand–Wrist–Forearm System
2.1.4. Elaboration of Data Collected by the Three Sensors
- Data synchronization. Sensors were read using the round robin method and each collected data was bound to a timestamp generated by the receiving system. As a consequence, all data received by the entire system were synchronized.
- Data visualization. Angles calculated were displayed on the x, y, z axes.
2.1.5. Sensor Tests
2.2. Phase 2: Application of the Developed Measurement System to a Real Case
2.2.1. Field Test Design
2.2.2. Evaluation of Data Recorded in Field Tests
- low level: the operator does not assume awkward postures for a long time, and no anomalous behaviors that could cause occupational diseases are found. This level is represented by the color green and corresponds to a low and acceptable risk.
- medium level: it is necessary to carefully monitor the worker. Some inappropriate postures are detected, which could generate occupational diseases in the future. This level is represented by the yellow threshold and represents moderate risk.
- high level: there is an incorrect working condition. The worker assumes incongruous positions for prolonged time. The risk level is represented by the color red and corresponds to a high and unacceptable risk level, which produces occupational diseases.
3. Results
3.1. Phase 1: Development and Testing of the Measuring System
3.2. Phase 2: Application of the Measurement System Developed on a Real Case
4. Discussion
4.1. The Developed Sensor
4.2. The Developed Measuring System Applied to Conventional Risk Assessment Methods
- wrist inclination;
- inclination angle;
- level of risk during the work phases.
- frequency of the repeated gesture;
- number of gestures per work cycle;
- worker break times;
- sequential pattern.
- arm position;
- wrist position.
4.3. The Developed Measuring System Applied to the Pruning of a Vine
4.4. Future Improvements
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
MSD | Musculoskeletal Disorders |
MAPO | Method to Assess the Risk of Patient Manual Handling in Hospital Wards |
NIOSH | National Institute for Occupational Safety and Health method |
OCRA | Occupational Repetitive Action |
REBA | Rapid Entire Body Assessment |
RULA | Rapid Upper Limb Assessment |
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Angle | Axis | Operator | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
O1 | O2 | O3 | O4 | ||||||||||
R1 | R2 | R3 | R1 | R2 | R3 | R1 | R2 | R3 | R1 | R2 | R3 | ||
15° | X | 15.30° | 14.99° | 15.23° | 15.18° | 15.22° | 15.00° | 14.97° | 15.20° | 15.01° | 15.00° | 15.02° | 15.01° |
Y | 14.98° | 15.07° | 14.95° | 15.30° | 15.20° | 15.15° | 15.02° | 15.15° | 14.97° | 15.30° | 15.01° | 14.98° | |
Z | 15.06° | 15.15° | 14.97° | 15.21° | 15.11° | 14.98° | 15.00° | 15.00° | 15.00° | 14.96° | 15.01° | 15.02° | |
45° | X | 45.01° | 44.90° | 45.03° | 45.01° | 45.10° | 44.98° | 45.06° | 45.03° | 45.00° | 45.10° | 44.97° | 45.01° |
Y | 44.99° | 45.10° | 45.10° | 45.00° | 49.80° | 45.02° | 45.00° | 45.01° | 45.10° | 45.01° | 45.50° | 45.00° | |
Z | 44.98° | 45.07° | 45.03° | 44.95° | 45.00° | 44.90° | 45.10° | 45.20° | 45.10° | 45.00° | 44.99° | 45.10° | |
60° | X | 59.78° | 60.10° | 60.50° | 59.98° | 60.00° | 60.01° | 60.10° | 60.11° | 60.11° | 59.88° | 60.40° | 60.01° |
Y | 60.10° | 59.97° | 60.12° | 60.20° | 60.09° | 59.98° | 59.99° | 60.01° | 60.11° | 60.09° | 60.20° | 60.02° | |
Z | 59.97° | 60.07° | 60.34° | 60.15° | 60.00° | 60.10° | 60.10° | 59.96° | 60.01° | 60.01° | 60.02° | 60.15° |
Angles | Axes | |||
---|---|---|---|---|
X (Avg. ± S.D.) | Y (Avg. ± S.D.) | Z (Avg. ± S.D.) | All (Avg. ± S.D.) | |
15° | 15.09° ± 0.12 A | 15.09° ± 0.13 A | 15.04° ± 0.08 A | 15.07° ± 0.11 A |
45° | 45.02° ± 0.06 B | 45.47° ± 1.37 B | 45.04° ± 0.08 B | 45.17° ± 0.80 B |
60° | 60.08° ± 0.20 C | 60.07° ± 0.08 C | 60.07° ± 0.11 C | 60.07° ± 0.13 C |
Cutting | Axis | Operator | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
O1 | O2 | O3 | O4 | ||||||||||
Cutting Sequence | Cutting Sequence | Cutting Sequence | Cutting Sequence | ||||||||||
S1 | S2 | S3 | S1 | S2 | S3 | S1 | S2 | S3 | S1 | S2 | S3 | ||
1 | X | 00.56° | 15.21° | 22.23° | 18.47° | 39.45° | 39.28° | 48.08° | 46.11° | 02.13° | 00.18° | 00.23° | 00.15° |
Y | 46.40° | 50.16° | 31.24° | 49.00° | 60.11° | 55.27° | 49.88° | 47.90° | 19.20° | 33.16° | 02.50° | 45.70° | |
Z | 45.11° | 10.61° | 27.85° | 30.16° | 15.84° | 22.26° | 03.71° | 10.28° | 14.65° | 47.80° | 46.00° | 47.84° | |
2 | X | 00.45° | 10.37° | 00.47° | 11.21° | 00.55° | 33.24° | 23.26° | 23.64° | 48.16° | 23.45° | 22.43° | 49.58° |
Y | 00.00° | 03.00° | 14.00° | 47.11° | 46.00° | 47.34° | 46.23° | 47.11° | 50.11° | 33.07° | 33.08° | 44.01° | |
Z | 10.11° | 23.15° | 15.30° | 20.10° | 22.17° | 10.21° | 10.33° | 08.45° | 20.70° | 25.50° | 30.80° | 48.90° | |
3 | X | 00.10° | 15.89° | 49.00° | 45.71° | 48.34° | 44.16° | 35.28° | 45.36° | 03.70° | 04.87° | 17.50° | 23.34° |
Y | 15.33° | 23.44° | 33.18° | 47.18° | 49.55° | 33.25° | 36.88° | 47.10° | 50.02° | 33.00° | 48.80° | 45.80° | |
Z | 47.00° | 10.70° | 11.23° | 48.55° | 49.74° | 50.22° | 55.00° | 60.00° | 15.20° | 49.70° | 34.17° | 46.11° | |
4 | X | 02.11° | 03.10° | 00.66° | 22.12° | 33.10° | 13.44° | 10.24° | 23.22° | 32.44° | 47.70° | 12.34° | 33.60° |
Y | 33.00° | 45.60° | 59.10° | 47.00° | 48.50° | 45.50° | 50.00° | 48.30° | 47.40° | 09.00° | 47.40° | 45.00° | |
Z | 47.33° | 39.00° | 11.14° | 15.56° | 47.88° | 50.10° | 49.33° | 45.60° | 47.60° | 48.50° | 48.45° | 49.00° | |
5 | X | 29.22° | 13.00° | 09.05° | 00.15° | 12.44° | 22.16° | 00.22° | 11.33° | 25.50° | 12.56° | 09.21° | 20.05° |
Y | 37.15° | 44.34° | 27.30° | 47.40° | 35.21° | 35.30° | 55.00° | 48.30° | 55.00° | 47.33° | 38.11° | 30.44° | |
Z | 15.29° | 13.34° | 04.22° | 09.56° | 45.70° | 36.00° | 50.10° | 46.80° | 47.00° | 45.80° | 46.32° | 38.00° | |
6 | X | 01.12° | 00.25° | 22.30° | 12.88° | 02.99° | 34.78° | 24.50° | 20.68° | 23.44° | 13.21° | 02.33° | 23.44° |
Y | 47.40° | 46.40° | 45.70° | 49.50° | 47.80° | 23.45° | 31.00° | 25.60° | 43.00° | 33.30° | 51.00° | 49.60° | |
Z | 47.33° | 46.13° | 46.00° | 09.34° | 48.01° | 12.00° | 54.20° | 45.80° | 47.40° | 49.40° | 46.88° | 48.00° | |
7 | X | 10.13° | 00.85° | 02.76° | 11.50° | 00.00° | 11.56° | 02.61° | 10.33° | 10.45° | 03.43° | 04.15° | 33.18° |
Y | 45.88° | 55.00° | 48.70° | 53.20° | 30.00° | 47.40° | 48.00° | 39.13° | 55.22° | 05.23° | 20.13° | 22.03° | |
Z | 45.80° | 05.11° | 47.40° | 55.80° | 10.20° | 10.66° | 10.15° | 15.28° | 49.00° | 47.88° | 45.15° | 23.08° |
Levels of Risk | Angles | Axes | ||||
---|---|---|---|---|---|---|
X | Y | Z | All | |||
High level | >45 | 10% | 57% | 52% | 40% | |
Medium level: uncertain. Potentially dangerous risk level | 20–45 | 33% | 35% | 18% | 28% | |
Low level: appropriate position | 0–20 | 57% | 8% | 30% | 32% |
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Share and Cite
Cividino, S.R.S.; Zaninelli, M.; Redaelli, V.; Belluco, P.; Rinaldi, F.; Avramovic, L.; Cappelli, A. Preliminary Evaluation of New Wearable Sensors to Study Incongruous Postures Held by Employees in Viticulture. Sensors 2024, 24, 5703. https://doi.org/10.3390/s24175703
Cividino SRS, Zaninelli M, Redaelli V, Belluco P, Rinaldi F, Avramovic L, Cappelli A. Preliminary Evaluation of New Wearable Sensors to Study Incongruous Postures Held by Employees in Viticulture. Sensors. 2024; 24(17):5703. https://doi.org/10.3390/s24175703
Chicago/Turabian StyleCividino, Sirio Rossano Secondo, Mauro Zaninelli, Veronica Redaelli, Paolo Belluco, Fabiano Rinaldi, Lena Avramovic, and Alessio Cappelli. 2024. "Preliminary Evaluation of New Wearable Sensors to Study Incongruous Postures Held by Employees in Viticulture" Sensors 24, no. 17: 5703. https://doi.org/10.3390/s24175703