IoT Wearables Dataset for Women's Safety: Stress Detection and Analysis

IoT Wearables Dataset for Women's Safety: Stress Detection and Analysis

The enhanced dataset is a sophisticated collection of simulated data points, meticulously designed to emulate real-world data as collected from wearable Internet of Things (IoT) devices. This dataset is tailored for applications in safety monitoring, particularly for women, and is ideal for developing machine learning models for distress or danger detection.

Number of Samples: 1000 entries

Attributes:

§  UserID: String (001-100), a unique identifier for each simulated user.

§  Timestamp: DateTime, indicating the date and time of each data entry, spanning the month of November 2023.a

§  Device Type: String, type of wearable (Earring, Ring, Shoe), indicating the source of data.

§  Pulse Rate (bpm): Integer (60-180 bpm), representing the user's pulse rate, with occasional outliers to mimic real-world variations.

§  Body Temperature (°F): Float (around 98.6°F), the user's body temperature, with normal distribution to simulate natural fluctuations.

§  Proximity to Unfamiliar Devices: String (Low, Medium, High), indicating the level of proximity to unfamiliar electronic devices.

§  Ambient Noise Level (dB): Integer (30-100 dB), reflecting the level of ambient noise.

§  Movement Pattern: String (Normal, Irregular), describing the user's movement pattern.

§  Stress Indicator: String (Yes/No), derived from physiological indicators like pulse rate and body temperature.

§  User Behavior: String (e.g., Active, Sedentary, Stressed, Calm), simulating different user profiles and behaviors.

Enhancements for Realism:

§  Diversity and Randomness: The dataset features varied and random data points, including outliers, to mirror the unpredictability of real-world scenarios.

§  Simulated User Behaviors: Introduces typical behaviors to add context and depth to the data, enhancing its applicability for real-world simulations.

Potential Applications:

§  Ideal for training and testing algorithms for real-time monitoring and automated distress detection.

§  Useful for research in safety technology, wearable IoT devices, and predictive analytics.

Ethical and Privacy Considerations:

The dataset is anonymized, ensuring compliance with ethical standards and privacy concerns.

Data Quality:

The dataset maintains a balance between realistic variations and data quality, ensuring its utility for analytical purposes.

This enhanced dataset provides a rich, realistic foundation for studies focusing on wearable technology, IoT applications, and machine learning, particularly in the context of women's safety and proactive risk detection.

The beneficiaries of this dataset are manifold, spanning from individual women who stand to gain enhanced personal safety to broader societal and technological domains. Women, as the primary end-users of wearable devices, would directly benefit from more nuanced and effective monitoring of their safety. This technology could offer them a greater sense of security in their daily lives, potentially reducing the incidence or severity of dangerous situations through timely alerts and interventions. On a societal level, the successful implementation of such a system could contribute to heightened public awareness and proactive measures regarding women's safety. Additionally, it could foster a culture of responsibility and vigilance in public spaces. Technologically, this research would benefit the fields of IoT and wearable technology by pushing the boundaries of how these devices can be used for critical, real-time applications. It would also offer valuable insights into the practical challenges and solutions involved in deploying such systems in diverse environments. Moreover, for the academic and scientific communities, this research could provide a rich dataset for developing and testing advanced machine learning models, potentially leading to breakthroughs in pattern recognition and predictive analytics. This, in turn, could have far-reaching implications beyond safety, influencing how similar technologies are applied in other areas of health and personal monitoring.