Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions
Abstract
:1. Introduction
1.1. Methodology
- RQ1: How can IIoT be integrated with yoga for automatic detection, guidance, and monitoring of yoga?
- RQ2: What are the various kinds of yoga, how does yoga affect human health, and what are the safety concerns while doing yoga?
- RQ3: What are the problems, challenges, and prospects for yoga in integration with IIoT?
1.1.1. Search Strategy
1.1.2. Study Selection
1.1.3. Data Extraction
1.2. Related Work
1.3. Contribution
- (i)
- This paper attempts to address the gap as presented in Table 1 by providing a structured and comprehensive overview of extensive research on the integration of yoga with the IIoT and its application.
- (ii)
- This paper discusses the state-of-the-art and critically analyzes it by conducting a thorough discussion. We focus on the current challenges in detecting and guiding yoga postures and highlight various issues that arise during wrong posture practice.
- (iii)
- This paper discusses various applications of yoga for human health and provides a guide for future approaches to overcome the limitations of existing approaches for different types of yoga detection and monitoring to enhance the adoption of IIoT-based yoga systems in real-world content.
2. Background of Yoga
2.1. Types of Yoga
2.1.1. Hatha Yoga
2.1.2. Asthanga Yoga
2.1.3. Hot or Bikram Yoga
2.1.4. Iyengar Yoga
2.1.5. Kundalini Yoga
2.1.6. Power Yoga
2.1.7. Restorative Yoga
2.1.8. Vinyasa Yoga
2.2. Differences between Pranayama and Yoga
- Focus: Yoga is a broader practice that focuses on physical postures, breathing techniques, meditation, and ethical principles. Conversely, pranayama is a specific practice that focuses exclusively on breathing techniques and control.
- Breath control: Pranayama emphasizes breath control, while yoga emphasizes physical postures. Pranayama techniques involve controlling the duration, depth, and rate of breathing to achieve specific effects on the body and mind.
- Benefits: Yoga and pranayama offer numerous physical and mental health benefits. Yoga improves flexibility, strength, balance, and cardiovascular health, while pranayama improves respiratory function, reduces stress, and improves mental focus.
- Practice: Yoga involves a more structured and varied practice, including a range of postures and breathing techniques, and may be performed for extended periods. On the other hand, pranayama practice typically involves more straightforward breathing techniques that are performed for shorter durations.
- Timing: Pranayama is often practiced as a standalone technique, while yoga is typically practiced as a part of a more extensive routine that includes meditation and ethical principles.
- Accessibility: Pranayama can be practiced by people of all ages and fitness levels, whereas some yoga postures may be challenging for beginners or those with physical limitations.
3. Application of Yoga on Human Health
3.1. Impact of Yoga on Psycho-Neurological Human Behavior
3.2. Impact of Yoga on Cardiovascular Disease, Hypertension
3.3. Impact of Yoga on Cancer
3.4. Impact of Yoga on Diabetes Mellitus
3.5. Impact of Yoga on Pregnancy and Reproductive Functions
3.6. Impact of Yoga on Bone
4. Safety Measures in Yoga Practice
4.1. Warm into Your Practice
4.2. Pay Attention
4.3. Adapt to Your Body and Try Using Props
4.4. Avoid the Red Flags
- Sharpness, shooting, numbness, or tingling down the limbs may cause nerve damage or indicate a problem that needs to be addressed.
- Anything that causes you to frown, grunt, or despise your yoga instructor.
- Intensity deep within the joint that may cause cartilage, tendons, or ligament damage.
- A stretch or engagement that you can smile through, even if it is difficult.
- Sensation in the muscle’s belly (thickest part), which is the safest place to feel when stretching.
4.5. Know Some Basic Anatomy
4.6. Talk to a Doctor
5. Detection of Yoga Using Intelligent Internet of Things
5.1. Sensor-Based Approach
5.1.1. Wearable Sensor
5.1.2. Infrared Sensors
5.1.3. RFID
5.1.4. Smart Mat
5.2. Vision-Based Approach
6. Performance Indicator
7. Discussion
8. Challenges and Future Directions of Yoga
- To identify the appropriate sensors and devices that can accurately capture data related to yoga practice, such as body movements, heart rate, and breathing patterns, is also challenging. These sensors should be non-intrusive and comfortable to wear during yoga practice.
- To develop algorithms and machine learning models to analyze the data collected from these sensors and provide valuable insights related to yoga practice, such as the effectiveness of different postures or the impact of specific breathing techniques.
- To ensure the security and privacy of the data collected by IIoT devices during yoga practice. Appropriate measures such as encryption and anonymization should be implemented to prevent unauthorized access and ensure data privacy.
- The essence of standardization in developing and implementing IIoT systems for yoga practice is to ensure the interoperability and compatibility across different devices and platforms.
- To ensure that the use of IIoT in yoga practice aligns with the values and principles of traditional yoga and does not compromise the integrity and authenticity of the practice.
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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[19] | [20] | [21] | [22] | [30] | [23] | [24] | [25] | [27] | [29] | This Survey | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Yoga Background | General Background | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ |
Types of Yoga | √ | |||||||||||
Safety Measures | √ | |||||||||||
Yoga vs. Pranayama | √ | √ | ||||||||||
Challenges | √ | √ | √ | √ | ||||||||
Detection of Yoga | Sensor-based | √ | √ | |||||||||
Vision-based | √ | √ | ||||||||||
Hybrid-based | √ | |||||||||||
Application | Single | √ | √ | √ | √ | √ | √ | |||||
Multiple | √ | √ | √ | √ | ||||||||
New Perspectives | √ |
Reference | Type of Yoga | Descriptions |
---|---|---|
[32] | Hatha Yoga | It employs physical techniques to retain and channel vital force or energy. It aligns, cleanses, and calms your mind, body, and spirit, allowing you to achieve deeper levels of meditation and spiritual awareness. It helps to increase stamina, flexibility, range of motion, and balance; as well as to decrease tension and promote mental calm. |
[33] | Asthanga Yoga | Physically demanding and challenging style. It is a strenuous practice that promises to increase physical stamina and flexibility. |
[34,35,36] | Hot or Bikram Yoga | It uses a fixed 26-posture series and two breathing exercises to help simulate the temperature. Bikram yoga has positive benefits on metabolic markers such as blood lipids, insulin resistance, and glucose tolerance. |
[37,38] | Iyengar Yoga | It is slower than Ashtanga or Bikram, but this form of yoga still calls for a great amount of attention to attain correct posture alignment and the capacity to hold the asanas for extended periods of time. Persistent lower back pain significantly decreased in trial participants who practiced Iyengar yoga. |
[39] | Kundalini Yoga | In the form of chanting or song, it combines movement, breath, and sound. Kundalini is meant to awaken the shakti, the spiritual force that resides at the base of your spine. It can positively impact our mood, concentration, blood pressure, metabolism, and strength, among other health benefits. |
[31] | Power Yoga | Less structured and more open to personal interpretation by teachers. Generally, more physical and performed at a faster tempo than other kinds of yoga. Increases flexibility while also strengthening the muscles. |
[40] | Restorative Yoga | Help with stress relief because lying in these postures for extended periods of time allows you to listen to your body’s signals and focus your mind. Nurses working night shifts reported that group restorative yoga sessions significantly reduced their psychological and physical stress reactions. |
[33] | Vinyasa Yoga | Also known as flow yoga or vinyasa flow. The word “vinyasa”, which means “special spot”, generally means connecting breath and movement. Vinyasa or flow is frequently used with slow, dynamic, or attentive movement to denote the intensity of practice and is more appropriate for newcomers to yoga than for those who have been practicing it for years. |
References | Approaches | Descriptions |
---|---|---|
[13,14,15,85,86,88,89,90,91,92,93,94,95] | Sensor-based approach | Multiple sensors are used to detect yoga postures, including wearable, infrared sensors, RFID, and smart mat. |
[96,97] | Vision-based approach | Relies on the camera for the input, which is further processed using intelligent approaches for the detection of the yoga postures. |
[98] | Logistics regression | An extension of ordinary regression; it is a powerful and popular technique for supervised classification for modeling a dichotomous variable for an associated label. |
[99] | Adaboost | An ensemble method to combine weak classifiers to create a powerful classifier. To attain high accuracy for the model, it continues to add learners until a robust classifier is reached. |
[100,101,102] | Random forest | In RF, each tree is reliant on values from a random vector that was randomly sampled and had a uniform distribution across all of the forest trees. |
[103] | Support vector machine (SVM) | It has two classifiers and is an SVM classifier. Nonetheless, a multiclass SVM is widely used because most issues involve multiple classes. |
[3,104] | K-nearest neighbor (KNN) | KNN saves all potential examples and categorizes them according to their similarities. It is primarily used with the pattern recognition method. |
[105] | Deep learning-based methods | Deep learning is essentially based on ANN and it can be compared to the human brain. |
[106,107,108,109] | AutoEncoder | A rich and versatile framework for discovering the salient features of data in an unsupervised manner. Used to drive the learning of a deep illustration of the volumetric human body structure. |
[103,110,111] | Convolutional neural networks (CNN)s | A great choice because they have proven to have a significant amount of potential for pose classification tasks. They can be trained directly on pictures or on key human skeleton joint locations. |
[112] | Recurrent neural networks (RNNs) | RNNs are useful for processing sequential data since they preserve a neuron’s prior data. RNNs have difficulty remembering the initial steps necessary to forecast the current task when there are too many intermediate steps in a yoga asana. |
[113] | Long short-term memory (LSTM) | A well-known RNN called an LSTM has the ability to naturally remember knowledge or data for sufficient lengths of time. The LSTM algorithm employs three gates: input, update, and forget. Resultantly, an LSTM will selectively ignore or recall the learned information. |
[114,115,116,117,118] | Deep neural networks (DNNs) | DNNs have demonstrated exceptional performance on visual classification functions. DNNs can capture the complete context of every body joint since each joint regressor uses the entire image as a signal. |
[119,120,121,122,123,124,125,126,127] | Hybrid approaches | Several algorithms make use of hybrid models. For example, SVM and Inception V3 are hybrid algorithms. Another study classified data using a hybrid 798 CNN–LSTM layer after extracting key points using OpenPose. |
Ref. | Asana (No.) | Method | Performance | Method |
---|---|---|---|---|
[86] | 6 | CNN, LSTM | 99.91% | Sensor-based |
[89] | 6 | CNN, LSTM | 89.29%, 96.31% | |
[90] | 5 | CNN | 96% | |
[88] | 10 | XBoost | 99.2% | |
[124] | 10 | RF, KNN, SVM, DT | 94.28% | Vision-based |
[14] | 26 | DCNN | 99.99% | |
[110] | 42 | CNN | 98.93% | |
[99] | 5 | Adaboost | 94.78% | |
[101] | 10 | RF | 96.47% | |
[103] | 4 | SVM | 94.9% | |
[3] | 4 | KNN | 93.1% | |
[114] | 17 | LSTM | 97.7% | |
[105] | 8 | CNN, SAE | 90%, 70% |
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Pal, R.; Adhikari, D.; Heyat, M.B.B.; Ullah, I.; You, Z. Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions. Bioengineering 2023, 10, 459. https://doi.org/10.3390/bioengineering10040459
Pal R, Adhikari D, Heyat MBB, Ullah I, You Z. Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions. Bioengineering. 2023; 10(4):459. https://doi.org/10.3390/bioengineering10040459
Chicago/Turabian StylePal, Rishi, Deepak Adhikari, Md Belal Bin Heyat, Inam Ullah, and Zili You. 2023. "Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions" Bioengineering 10, no. 4: 459. https://doi.org/10.3390/bioengineering10040459
APA StylePal, R., Adhikari, D., Heyat, M. B. B., Ullah, I., & You, Z. (2023). Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions. Bioengineering, 10(4), 459. https://doi.org/10.3390/bioengineering10040459