Internet of Things-Driven Smart Furniture Systems for Human-Centered Personalization: Experimental Evaluation Using Behavioral Sensor Data

Summary

This study presents a smart furniture system that uses deep reinforcement and federated meta-learning to personalize user experiences in real time. By modeling user behavior as a sequential decision process, it reduces discomfort by 43% and energy use by 21% while preserving privacy. The results demonstrate a robust, responsive framework suitable for health-aware workspaces and smart homes.

Abstract

This paper introduces an advanced Internet of Things (IoT)-driven smart furniture system designed to dynamically adapt to individual users by integrating deep reinforcement learning with federated meta-learning. Personalization is formulated as a Markov decision process, enabling the system to make optimized, sequential adjustments tailored to each user’s behavior. To estimate hidden ergonomic preferences in real time, an adaptive Kalman filter is applied, while a sparse autoencoder reduces raw sensor signals by 82 %, preserving key temporal features essential for accurate modeling. In a comprehensive user study involving 48 participants and more than 160,000 time-series sensor samples, the framework significantly reduced cumulative user dissatisfaction by 43 % and cut energy consumption by 21 %, compared with conventional rule-based control systems. Real-time adaptations occur with an average latency of 280 ms, and constraints for ergonomics are upheld in 95 % of use cases, confirming the system operates swiftly and safely. Federated learning (FL) enables privacy-preserving collaboration across distributed furniture units. Training converges to 87 % of global performance within 30 global iterations, without any raw data exchange, reinforcing both scalability and data privacy. These empirical results strongly support the framework’s suitability for deployment in health-aware workspaces, smart homes, and eldercare environments, delivering a robust, responsive, and interpretable solution for enriching human–furniture interaction.

Introduction

The Internet of Things (IoT) has enabled the integration of sensing, computation, and actuation into physical environments, supporting adaptive systems capable of context-aware interaction and real-time decision-making^1,2,3 Within this paradigm, smart furniture systems embed pressure sensors, inertial measurement units, environmental monitors, microcontrollers, wireless communication modules, and artificial intelligence algorithms to monitor posture, environmental conditions, and interaction patterns⁴. Unlike conventional ergonomic equipment, these systems aim to enable continuous personalization through data-driven behavioral modeling and adaptive feedback mechanisms^5,6.

The increased prevalence of hybrid work models, sedentary lifestyles, and aging populations has intensified research interest in ergonomically responsive furniture capable of supporting comfort, productivity, and health monitoring^7,8,9,10. Smart furniture platforms have been explored in residential, office, healthcare, and educational contexts, where embedded intelligence allows furniture to function as an interactive interface between users and their environments^11,12. Recent research has investigated reinforcement learning and federated learning for adaptive personalization in distributed systems^13,14,15. Privacy-preserving reinforcement learning methods for IoT-enabled applications have highlighted trade-offs between adaptivity and data protection¹³. Federated learning frameworks in edge computing have addressed communication efficiency and decentralized optimization challenges¹⁴. Deep reinforcement learning has demonstrated effectiveness in digital personalization services¹⁵. However, most prior work has focused on virtual service platforms rather than embodied physical systems such as smart furniture¹⁶.

Physical smart furniture environments introduce additional constraints, including actuator dynamics, mechanical safety requirements, energy consumption limitations, sensor noise, latency constraints, and multi-user interaction complexity¹⁷. For example, posture-monitoring smart chairs and sleep-tracking smart beds must integrate biomechanical sensing with environmental coordination mechanisms such as heating, ventilation, and air conditioning (HVAC) systems¹⁸. Personalization in these contexts, therefore, requires dynamic modeling of physiological, behavioral, and environmental signals rather than static preference mapping^19,20.

Embedded IoT sensors integrated into structural components such as seat cushions, armrests, backrests, and tabletops can capture multivariate time-series data, including motion (accelerometers, gyroscopes), pressure distribution, thermal measurements, acoustic context, and physiological indicators such as electrodermal activity (EDA) and heart rate variability^21,22. The embedded sensors operate under low-power consumer safety standards and emit only negligible non-ionizing electromagnetic radiation. Their exposure levels remain well below international safety thresholds. Raw sensor signals are typically high-dimensional, noisy, and context-dependent²³, necessitating structured signal processing, feature extraction, and sensor fusion to construct reliable user state representations²⁴.

Machine learning methods including supervised classification, unsupervised clustering, and deep neural networks have been applied to behavioral modeling in smart environments^25,26. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) capture temporal dependencies in user movement, while clustering approaches segment users into behavioral groups²⁶. Although these techniques improve pattern recognition, they do not inherently optimize sequential control decisions under uncertainty. Rule-based control strategies commonly implemented in ergonomic systems map sensor thresholds to predefined actions but lack adaptive learning capability²⁷. Consequently, static mappings may fail to accommodate evolving preferences and contextual variability over time²⁸.

Multi-user environments further require dynamic contextual differentiation and adaptive profile switching to maintain performance across heterogeneous users²⁹. Achieving scalable personalization therefore requires models capable of longitudinal behavioral learning while preserving privacy and computational efficiency³⁰. Embedding contextual intelligence directly into furniture infrastructure enhances interaction responsiveness; however, distributed IoT systems must also address privacy protection and secure data handling constraints^31,32. To address these challenges, this study proposes a unified intelligent control framework for IoT–enabled smart furniture systems that integrates reinforcement learning, adaptive state estimation, and federated meta-learning. Personalization is formulated as a Markov decision process (MDP), enabling sequential optimization of user comfort and energy efficiency under stochastic transitions. Reinforcement learning continuously refines control policies based on cumulative reward feedback, overcoming limitations of heuristic or static mapping approaches.

An adaptive Kalman filter is incorporated to estimate latent user preference states from noisy multivariate observations, separating measurement noise from underlying ergonomic intent and improving control stability. This filter is a recursive probabilistic state-estimation algorithm used to infer latent ergonomic preferences from noisy observations. Its role in separating measurement noise from hidden user intent has been explicitly clarified. Federated learning enables distributed model aggregation without raw data exchange, while meta-learning accelerates adaptation to new users through optimized initialization across heterogeneous populations. Figure 1 presents the multi-layer architecture of the proposed smart furniture system, organized into sensing, inference, personalization, and ethical infrastructure layers. The diagram illustrates how sensor acquisition, edge/cloud intelligence, adaptive control, and secure data management are integrated within a unified real-time personalization framework.

The primary objective of this study is to design and experimentally evaluate a scalable, energy-aware, and privacy-preserving personalization architecture for smart furniture systems. The proposed framework integrates sensing, representation learning, probabilistic state estimation, sequential decision optimization, and federated collaboration within a cohesive structure. Experimental validation using prototype smart furniture units demonstrates measurable improvements over heuristic control approaches in terms of user dissatisfaction reduction and energy efficiency. By extending reinforcement learning and federated adaptation from digital services to embodied physical environments, this work establishes a technically grounded pathway toward interpretable and experimentally validated intelligent furniture systems.

Protocol

Ethical approval was not required for this study. The experimental procedures involved non-invasive ergonomic interaction with prototype smart furniture units, without medical intervention, clinical testing, or collection of identifiable personal health information. All recorded sensor data were anonymized prior to storage and analysis. Participation was voluntary, and no sensitive or clinical data were collected.

1. Hardware and system preparation

1. Assemble the smart furniture unit
   1. Install a 16-channel capacitive pressure sensor array in the seat cushion.
   2. Mount a 6-axis inertial measurement unit (accelerometer + gyroscope) on the backrest.
   3. Install temperature, humidity, ambient light, and sound sensors.
   4. Integrate optional electrodermal activity (EDA) and heart-rate sensor modules.
   5. Connect all sensors to an ESP32 microcontroller via I2C/SPI interfaces.
2. Configure actuation system
   1. Install servo motors for height and tilt adjustment.
   2. Set tilt adjustment range to ±15°.
   3. Set height adjustment range to 40–70 cm.
   4. Limit the actuation step size to ΔS_max = 2° per update cycle.
3. Configure compute platform
   1. Install Ubuntu 20.04 on NVIDIA Jetson Nano (4 GB RAM) or equivalent.
   2. Install Python 3.9.
   3. Install PyTorch 1.13, NumPy, SciPy, and Scikit-learn.
   4. Enable CUDA acceleration if a GPU is available.

2. Data acquisition

1. Configure sampling
   1. Set sensor sampling rate to 20 Hz.
   2. Synchronize all sensor streams using timestamp alignment.
   3. Store data in CSV format.
2. Conduct Interaction Sessions
   1. Recruit 48 adult participants.
   2. Record 60-minute seated interaction sessions per participant.
   3. Collect subjective comfort scores on a 1–5 Likert scale.
3. Partition dataset
   1. Remove participant identifiers.
   2. Split the dataset into 70% training, 15% validation, and 15% test sets.

3. Data pre-processing

1. Filter signals
   1. Apply a low-pass Butterworth filter (cutoff frequency = 5 Hz).
   2. Normalize each sensor channel to zero mean and unit variance.
2. construct state representation.
   1. Concatenate furniture state S(t) with sensor vector X_i(t).
   2. Segment signals using sliding windows (window size = 50-time steps).

4. Sparse autoencoder training

1. Define architecture.
   1. Set input dimension d.
   2. Set latent dimension p = 0.3d.
   3. Use ReLU activation.
2. Train model
   1. Use Adam optimizer (learning rate = 0.001).
   2. Set batch size = 64.
   3. Train for 100 epochs.
   4. Minimize loss:
      1. Define L_SE as the sparse autoencoder loss function.
      2. Define  as the original input sensor data matrix.
      3. Define n is the number of samples and is the number of sensor features.
      4. Define  as the reconstructed output generated by the decoder network.
      5. Define  as the squared Euclidean reconstruction error.
      6. Define W⁽¹⁾ as the weight matrix of the first encoder layer.
      7. Define  as the L1 norm of the encoder weights, promoting sparsity in the learned representation.
      8. Define λ > 0 as the sparsity regularization coefficient controlling the trade-off between reconstruction fidelity and sparsity.
      9.   
      10. Set λ = 0.001.
3. Export encoder
   1. Save trained encoder weights.
   2. Freeze encoder parameters for reinforcement learning phase.

5. Markov decision process formulation

1. Define State Space
   1. Define S(t) as furniture configuration.
   2. Define h(t) as compressed sensor embedding.
   3. Construct state vector .
2. Define action space
   1. Define 
   2. Constrain 
3. Define reward function
   1. Compute dissatisfaction
      1. Define D(t) as the instantaneous user dissatisfaction at time step t.
      2. Define  as the actual furniture state vector at time.
      3. Define m as the number of controllable mechanical parameters.
      4. Define  as the ideal (optimal) ergonomic state preferred by the user at time.
      5. Define  as the squared Euclidean norm.
         
   2. Define reward
      Define  as the squared Euclidean magnitude of the control adjustment.
      
      Set λ = 0.05

6. Kalman filter estimation

1. Initialization of the latent preference state and covariance.
   1. Set initial state estimate 
   2. Set initial covariance P(0) = 1
2. Prediction of latent preference dynamics
   1. Predict the latent ergonomic preference state at the next time step.
      1. Define  as the predicted evolution of the latent user preference state.
      2. Define  as the influence of the previously applied furniture adjustment on the predicted latent preference at time .
   2. Propagate the uncertainty of the predicted state estimate.
      1. Define  as the propagated covariance of the latent state estimate.
      2. Define A as the system dynamics matrix.
      3. Define Q as the process noise covariance matrix.
         
      4. Set Q = 0..01
3. Measure the correction of the latent preference estimate.
   1. Compute Kalman gain K(t).
   2. Update state estimate and covariance.
   3. Set R = 0.05

7. DDPG training

1. Initialize networks
   1. Use two hidden layers (128 neurons each) for the actor and critic.
   2. Initialize target networks.
2. Set hyperparameters
   1. Actor learning rate = 1 × 10^-4
   2. Critic learning rate = 1 × 10^-3.
   3. Discount factor γ = 0.99
   4. Soft update τ = 0.005
   5. Replay buffer size = 100,000
3. Train policy
   1. Train for 200,000 interaction steps.
   2. Sample mini-batches of size 64.
   3. Update critic before actor.

8. Federated meta-learning

1. Distribute the global model.
   1. Send global model to edge devices.
2. Perform local training
   1. Train locally for 5 epochs per round.
   2. Compute gradients.
3. Aggregate
   1. Perform weighted averaging.
   2. Repeat for 30 communication rounds.

9. Energy-constrained optimization

1. Define constraint
   Define ε_max as the maximum allowable dissatisfaction threshold
   1. Enforce
2. Apply Karush–Kuhn–Tucker (KKT) conditions
   1. Define  as the gradient of the Lagrangian function L with respect to the control adjustment vector ΔS.
   2. Compute .
   3. Update Lagrange multiplier.

10. Baseline evaluation

1. Implement baselines
   1. Implement rule-based threshold control.
   2. Implement tabular Q-learning.
   3. Implement random forest predictor.
   4. Implement transformer-based policy.
2. Performance evaluation of the implemented control strategies
   1. Measure cumulative dissatisfaction.
   2. Measure energy consumption.
   3. Measure latency.

11. Expected outputs

1. Obtain trained personalization policy.
2. Obtain reduced dissatisfaction compared to baseline.
3. Obtain energy-efficient actuation behavior.
4. Save trained model weights for reproducibility.

Results

Hardware integration and system validation
The smart furniture system operated reliably under continuous multimodal sensing and real-time actuation. All sensors streamed synchronized data at 20 Hz without observable packet loss. Mechanical limits were consistently enforced, with tilt constrained to ±15°, height limited to 40–70 cm, and step changes bounded by ΔS_max = 2⁰ per control cycle. Edge inference remained stable throughout prolonged sessions, confirming the feasi...

Discussion

The present study demonstrates that a hybrid framework integrating reinforcement learning, adaptive Kalman filtering, sparse representation learning, and federated meta-learning can significantly improve embodied personalization in smart furniture systems under real-time constraints. Across 48 participants, the framework achieved a 43.2% reduction in cumulative dissatisfaction relative to rule-based control (p < 0.001) and a 21.2% reduction in total energy consumption (p < 0.001), while maintaining mean actuation l...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Actuators	Generic Manufacturer	N/A	Adjustments
Adaptive Kalman Filter	Custom Implementation	N/A	State estimation algorithm
Bio-signal (EDA/HRV/Resp.)	Generic Manufacturer	N/A	Health awareness
Connectivity	Generic Manufacturer	N/A	Network uplink
DDPG (Actor–Critic)	OpenAI Baselines / PyTorch	DDPG Module	Deep deterministic policy gradient RL
Edge MCU	Generic Manufacturer	N/A	Low-latency
Federated Learning	Google Research	FedAvg Protocol	Federated averaging aggregation method
Federated Meta-Learning	Custom / Open-source	N/A	Meta-initialization minimizes meta-loss
IMU (Accel/Gyro)	TDK InvenSense	MPU-6050	6-axis accelerometer and gyroscope module
Light Sensor	ROHM Semiconductor	BH1750FVI	Digital ambient light sensor
MDP Formulation	Custom Implementation	N/A	Markov decision process modeling
Microphone	Adafruit Industries	1713 (MAX9814)	Electret microphone amplifier module
Pressure Matrix	Interlink Electronics	FSR-400 Series	16-channel capacitive pressure sensing array
Sparse Autoencoder (SAE)	Meta AI	PyTorch 1.13	Neural network training framework
Thermal Sensor	Adafruit Industries	385 (DHT22)	Digital temperature and humidity sensor