Cameras have long dominated the world of human activity recognition, powering everything from fall-detection systems for elderly care to gesture interfaces in smart homes. But they come with an uncomfortable trade-off: to monitor a person’s wellbeing, a camera must record their most private moments. A new study from researchers at Aston University in Birmingham offers a way to have the benefits without the intrusion. Vincent Gbouna Zakka, Luis J. Manso and Zhuangzhuang Dai have developed SEdgeNet, a graph-based neural network that recognises human activities using millimetre-wave radar, a sensor that perceives people as faint clouds of points rather than images, and that works just as well in complete darkness as in bright light.
Millimetre-wave radar, the same class of technology used in automotive collision avoidance and next-generation wireless communications, emits radio waves in the millimetre band and listens for reflections. When those reflections bounce off a moving person, the radar’s signal processing pipeline produces a point cloud: a scattered set of three-dimensional coordinates tracing the surfaces and limbs that returned the signal. Unlike the dense, well-structured point clouds produced by lidar or depth cameras, radar point clouds are notoriously sparse, often containing only a handful of points per frame, and they are contaminated by multipath reflections and hardware noise. That sparsity and irregularity is precisely what has made them so difficult for deep learning systems to interpret.
The Aston team’s insight begins with a widely used building block of point-cloud deep learning called EdgeConv, introduced originally in the Dynamic Graph CNN architecture. EdgeConv treats each point as a node in a graph, connects it to its k nearest neighbours, and computes features along the edges connecting them, capturing the local geometry of the scene. The problem, as the researchers point out, is that conventional EdgeConv is deterministic and indiscriminate: it always uses every one of its k nearest neighbours in every forward pass, faithfully incorporating noisy, redundant or uninformative points into the representation. On clean, dense benchmark datasets from 3D vision, that determinism is a virtue. On sparse, noisy radar data, it becomes a liability, because the same corrupted neighbourhoods poison the features pass after pass.
SEdgeNet’s central contribution is a redesigned operator the authors call Stochastic Edge Convolution, or SEdgeConv, which is built from two complementary mechanisms. The first, Stochastic Neighbour Sampling, randomly selects only a subset of each point’s k nearest neighbours during each forward pass, governed by a sampling ratio that determines what fraction of the neighbourhood is used. The idea echoes dropout-style regularisation techniques from the graph neural network literature, such as DropEdge: by forcing the network to produce useful representations from constantly varying, randomly drawn subsets of neighbours, the model is discouraged from leaning on any single noisy point or fixed local configuration. Crucially, the researchers note, this is not a permanent loss of information. Across the many forward passes of training, the full neighbourhood structure remains accessible; the stochasticity simply prevents overreliance on any particular slice of it.
The second mechanism, Edge-wise Factorised Convolution, tackles the computational side of the problem. Standard EdgeConv applies a dense transformation across all edge feature channels jointly, which is expensive. EFC instead adapts the depthwise separable convolution paradigm, originally developed to make convolutional networks efficient on regular image grids, to the irregular edge features produced by stochastically sampled neighbourhoods. Each channel of the edge feature tensor is first transformed independently, and the per-channel responses are then fused through a pointwise linear projection. The authors stress that the efficiency gain comes from coupling this factorised processing with the dynamically varying graph structure created by the stochastic sampling, rather than from the factorisation scheme alone. Neighbour features are then aggregated with symmetric max pooling, yielding a permutation-invariant representation that does not depend on the ordering of points.
Around these operators, SEdgeNet stacks multiple SEdgeConv layers and concatenates their outputs along the channel dimension, forming a multi-scale embedding that preserves both fine-grained local geometry and more abstract global structure. A dual pooling strategy, combining adaptive max pooling with adaptive average pooling, distils this embedding into a fixed-length global descriptor; max pooling emphasises salient structures while average pooling encodes overall shape statistics. The pooled vectors feed a three-layer multilayer perceptron classifier that outputs the predicted activity class. The model is trained with stochastic gradient descent, a cosine annealing learning rate schedule, cross-entropy loss and early stopping, with all reported results averaged over three independent training runs using different random seeds.
The team evaluated SEdgeNet on two public mmWave radar datasets. The first, MMActivity, contains 93 minutes of point cloud data covering five activities performed by two subjects, captured with a Texas Instruments IWR1443BOOST sensor and processed into 12,097 training and 3,538 test samples using two-second windows. The second, MiliPoint, is far more demanding: 49 activities performed by 11 subjects, recorded with a TI IWR1843 radar and split 80-10-10 for training, validation and testing. On MMActivity, SEdgeNet outperformed the best previous model by 0.72 percent in accuracy, 0.71 percent in precision, 2.7 percent in recall and 0.71 percent in F1 score. On MiliPoint, the gains were larger still: 5.42 percent in accuracy, 4.91 percent in precision, 4.81 percent in recall and 5.26 percent in F1 score, establishing new state-of-the-art results on both benchmarks.
What makes those numbers remarkable is that they were achieved while using fewer computational resources than every baseline compared. SEdgeNet has the fewest parameters and the lowest multiply-accumulate count among the tested methods. Against DGCNN, the most efficient baseline, it uses 0.12 million fewer parameters and 0.02 giga-MACs less computation while achieving dramatically higher accuracy. Against AGConv, the most accurate baseline, it improves accuracy by 5.42 percent while cutting parameters by 0.52 million and computation by 8.43 giga-MACs. In controlled experiments swapping SEdgeConv for standard EdgeConv within an otherwise identical architecture, the stochastic version improved accuracy by 9.4 percent on MiliPoint and 0.44 percent on MMActivity, and required 3.08 giga-MACs less computation at a neighbourhood size of five. Ablation studies confirmed that both SNS and EFC contribute independently, and that combining them yields the best accuracy on both datasets alongside the lowest computational cost of 0.34 giga-MACs.
The hyperparameter analysis revealed instructive differences between the two datasets. MMActivity, with only five relatively separable classes, peaked with two layers and 35 nearest neighbours, reaching 98.12 percent accuracy, and its performance was dominated by network depth rather than neighbourhood size. MiliPoint, with 49 harder-to-distinguish classes, required a larger neighbourhood of 50 neighbours and five layers to reach 85.70 percent, suggesting that harder classification tasks demand both wider receptive fields and deeper networks to extract sufficiently discriminative geometric structure. The optimal sampling ratio also differed, at 0.6 for MMActivity and 0.3 for MiliPoint, though the authors found that a range of 0.3 to 0.5 delivers near-optimal accuracy on both datasets and recommend it as a practical default. Because the stochastic sampling remains active at inference time, the team also examined a Monte Carlo-style deployment strategy: averaging predictions over multiple forward passes. On MMActivity, accuracy rose from 97.72 percent with a single pass to a peak of 98.34 percent at six passes, at a still-modest cost of 2.04 giga-MACs.
Perhaps most compelling is the demonstration that the system runs in real time on embedded hardware. The researchers deployed their trained pipeline on a Jetson Nano mounted on a Robotino robot, with an AWR1843BOOST radar feeding data over USB. In a proof-of-concept trial, a single subject performed sitting, standing and walking freely in an indoor room, and the system achieved 99 percent classification accuracy, recognising actions under both lit and dark conditions. Average preprocessing latency was 12.18 milliseconds and model inference averaged 15.14 milliseconds, for a combined end-to-end latency of 27.32 milliseconds, comfortably within real-time requirements. The authors are careful to frame this as a feasibility demonstration rather than a comprehensive deployment study, noting that broader evaluation across multiple subjects, more activities and varying distances remains future work. Even so, the combination of privacy preservation, darkness-robust sensing, state-of-the-art accuracy and embedded real-time performance positions SEdgeNet as a promising foundation for assistive robotics, ambient intelligence and independent-living support, where the goal is to watch over people’s wellbeing without ever truly watching them.
Subject of Research: Graph neural network for human activity recognition from sparse millimetre-wave radar point clouds
Article Title: SEdgeNet: stochastic edge network for human activity recognition using sparse point cloud
Article References: Zakka, V. G., Manso, L. J., & Dai, Z. (2026). SEdgeNet: stochastic edge network for human activity recognition using sparse point cloud. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-026-05133-z
Image Credits: AI Generated
DOI: 10.1007/s12652-026-05133-z
Keywords: SEdgeNet, millimetre-wave radar, human activity recognition, point cloud, graph neural network, stochastic neighbour sampling, edge convolution, privacy-preserving sensing, embedded AI, ambient intelligence, depthwise separable convolution, assistive robotics
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Tags: ambient intelligenceassistive roboticsautonomous vehicle collision avoidance sensorsdepthwise separable convolutionedge convolutionembedded AIgesture recognition with radarGraph neural networkgraph-based neural networks for human detectionhuman activity recognitionhuman activity recognition using radar sensorsmillimetre-wave radarmillimetre-wave radar technologynext-generation wireless communication sensorsnon-visual human activity sensingpoint cloudprivacy concerns in surveillance technologyprivacy-preserving activity monitoringprivacy-preserving sensingradar versus camera monitoringradar-based fall detection systemsSEdgeNetsparse point cloud analysisstochastic neighbour sampling
