# SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation

arXiv: https://arxiv.org/abs/1701.08886 · HTML: https://mlsanity.com/paper/1701.08886/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2017.

## Built on by (most cited first, 20 of 20)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)](https://mlsanity.com/paper/2205.13741/) (2022)
- [ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences](https://mlsanity.com/paper/2206.05291/) (2022)
- [Time-series Generation by Contrastive Imitation](https://mlsanity.com/paper/2311.01388/) (2023)
- [Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities](https://mlsanity.com/paper/2405.20959/) (2024)
- [A Spectral Enabled GAN for Time Series Data Generation](https://mlsanity.com/paper/2103.01904/) (2021)
- [A Survey of Transformer Enabled Time Series Synthesis](https://mlsanity.com/paper/2406.02322/) (2024)
- [KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI](https://mlsanity.com/paper/2401.00193/) (2023)
- [BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition](https://mlsanity.com/paper/2208.03647/) (2022)
- [Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices](https://mlsanity.com/paper/2402.03714/) (2024)
- [Modeling Time-Series and Spatial Data for Recommendations and Other Applications](https://mlsanity.com/paper/2212.13259/) (2022)
- [Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables](https://mlsanity.com/paper/2110.05732/) (2021)
- [Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows](https://mlsanity.com/paper/2307.10305/) (2023)
- [Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs](https://mlsanity.com/paper/2412.06389/) (2024)
- [DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks](https://mlsanity.com/paper/2508.21340/) (2025)
- [Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks](https://mlsanity.com/paper/2201.06147/) (2022)
- [PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings](https://mlsanity.com/paper/2204.13597/) (2022)
- [UserBoost: Generating User-specific Synthetic Data for Faster Enrolment into Behavioural Biometric Systems](https://mlsanity.com/paper/2407.09104/) (2024)
- [AI and Generative AI Transforming Disaster Management: A Survey of Damage Assessment and Response Techniques](https://mlsanity.com/paper/2505.08202/) (2025)
- [Preserving Temporal Dynamics in Time Series Generation](https://mlsanity.com/paper/2604.27182/) (2026)

## Abstract (arXiv)

Our ability to synthesize sensory data that preserves specific statistical properties of the real data has had tremendous implications on data privacy and big data analytics. The synthetic data can be used as a substitute for selective real data segments,that are sensitive to the user, thus protecting privacy and resulting in improved analytics.However, increasingly adversarial roles taken by data recipients such as mobile apps, or other cloud-based analytics services, mandate that the synthetic data, in addition to preserving statistical properties, should also be difficult to distinguish from the real data. Typically, visual inspection has been used as a test to distinguish between datasets. But more recently, sophisticated classifier models (discriminators), corresponding to a set of events, have also been employed to distinguish between synthesized and real data. The model operates on both datasets and the respective event outputs are compared for consistency. In this paper, we take a step towards generating sensory data that can pass a deep learning based discriminator model test, and make two specific contributions: first, we present a deep learning based architecture for synthesizing sensory data. This architecture comprises of a generator model, which is a stack of multiple Long-Short-Term-Memory (LSTM) networks and a Mixture Density Network. second, we use another LSTM network based discriminator model for distinguishing between the true and the synthesized data. Using a dataset of accelerometer traces, collected using smartphones of users doing their daily activities, we show that the deep learning based discriminator model can only distinguish between the real and synthesized traces with an accuracy in the neighborhood of 50%.