Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data

Khan, Yusuf Ahmed; Imaduddin, Syed; Singh, Yash Pratap; Wajid, Mohd; Usman, Mohammed; Abbas, Mohamed

doi:10.3390/s23031275

Cited by 12 publications

(4 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data collected from motion and inertial sensors, such as accelerometer and gyroscope, have been applied to Long short-term memory (LSTM)-based deep neural network to predict human physical activities, such as the papers proposed by [19,20]. In this study, data from the two experiments were used for prediction using a deep learning algorithm.…”

Section: Discussionmentioning

confidence: 99%

Predicting Intoxication Using Motorcycle and Head Movements of Riders Wearing Alcohol Intoxication Goggles

Seva

Rosario

Peñafiel

et al. 2023

Safety

View full text Add to dashboard Cite

The movement of a motorcycle is one of the critical factors that influences the stability of the ride. It has been established that the gait patterns of drunk and sober people are distinct. However, drunk motorcycle (MC) drivers’ balance has not been investigated as a predictor of intoxication. This paper characterized and used MC and head movements, such as pitch and roll, to predict intoxication while riding. Two separate experiments were conducted to monitor MC and head movement. Male participants were recruited between the ages of 23 and 50 to participate in the study. Participants used alcohol intoxication goggles (AIG) to simulate blood alcohol content (BAC) while driving on a straight path. Placebo goggles were used for control. Results showed that pitch and roll amplitudes of the MC could distinguish drivers wearing placebo and AIGs, as well as the pitch and roll frequency of the head. Deep learning can be used to predict the intoxication of MC riders. The predictive accuracy of the algorithm shows a viable opportunity for the use of movement to monitor drunk riders on the road.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting Intoxication Using Motorcycle and Head Movements of Riders Wearing Alcohol Intoxication Goggles

Seva

Rosario

Peñafiel

et al. 2023

Safety

View full text Add to dashboard Cite

show abstract

“…In reference [9], the authors have collected human sports activity data from three-axis accelerometers and explored high-precision recognition methods based on deep learning. Reference [10] applies sensors embedded in smartphones to collect human activity data, achieving high-precision recognition based on neural-network models. Reference [11] uses multiple wearable sensors (such as accelerometers, magnetometers, etc.)…”

Section: Contact Action Recognition Methodsmentioning

confidence: 99%

CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion

Niu,

He,

Fang

et al. 2024

Sensors

View full text Add to dashboard Cite

The recognition of human activity is crucial as the Internet of Things (IoT) progresses toward future smart homes. Wi-Fi-based motion-recognition stands out due to its non-contact nature and widespread applicability. However, the channel state information (CSI) related to human movement in indoor environments changes with the direction of movement, which poses challenges for existing Wi-Fi movement-recognition methods. These challenges include limited directions of movement that can be detected, short detection distances, and inaccurate feature extraction, all of which significantly constrain the wide-scale application of Wi-Fi action-recognition. To address this issue, we propose a direction-independent CSI fusion and sharing model named CSI-F, one which combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Specifically, we have introduced a series of signal-processing techniques that utilize antenna diversity to eliminate random phase shifts, thereby removing noise influences unrelated to motion information. Later, by amplifying the Doppler frequency shift effect through cyclic actions and generating a spectrogram, we further enhance the impact of actions on CSI. To demonstrate the effectiveness of this method, we conducted experiments on datasets collected in natural environments. We confirmed that the superposition of periodic actions on CSI can improve the accuracy of the process. CSI-F can achieve higher recognition accuracy compared with other methods and a monitoring coverage of up to 6 m.

show abstract

“…The realization of future trends will involve advancements in signal processing algorithms specifically tailored for flexible magnetic field sensors. AI algorithms and machine learning (ML) techniques will play a vital role in extracting meaningful information from sensor data, enabling precise measurements and reliable analysis. − …”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Flexible Magnetic Field Nanosensors for Wearable Electronics: A Review

Mostufa

Yari

Rezaei

et al. 2023

ACS Appl. Nano Mater.

View full text Add to dashboard Cite

Flexible magnetic field nanosensors hold immense potential for wearable electronics, offering a range of advantages such as comfort, real-time health monitoring, motion sensing, durability, and seamless integration with other sensors. They are expected to revolutionize wearable technologies and drive innovation in various domains, enhancing the overall user experience. In this review, we provide an overview of recent advances in flexible magnetic field nanosensors, including flexible Hall sensors, flexible magnetoresistive (MR) sensors such as giant magnetoresistance (GMR), magnetic tunnel junction (MTJ), and anisotropic magnetoresistance (AMR) sensors, flexible fluxgate sensors, and flexible giant magnetoimpedance (GMI) sensors. We discuss different fabrication methods and real-life applications for each type of sensor as well as the technical challenges faced by these sensors. The use of these flexible nanosensors opens more possibilities for human−computer interaction and presents exciting opportunities for wearable technology in diverse fields. The robustness of these sensors along with the trend to reduce energy consumption will continue to be important research areas. Future trends in flexible magnetic field nanosensors include energy harvesting from the body, miniaturization and lower power consumption, improved durability and reliability, and reduced cost. These advancements have the potential to drive the widespread adoption of flexible magnetic field nanosensors in wearable devices, enabling innovative applications and enhancing the overall user experience.

show abstract

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data

Cited by 12 publications

References 60 publications

Predicting Intoxication Using Motorcycle and Head Movements of Riders Wearing Alcohol Intoxication Goggles

Predicting Intoxication Using Motorcycle and Head Movements of Riders Wearing Alcohol Intoxication Goggles

CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion

Flexible Magnetic Field Nanosensors for Wearable Electronics: A Review

Contact Info

Product

Resources

About