English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper we propose mmFall - a novel fall detection system, whichcomprises of (i) the emerging millimeter-wave (mmWave) radar sensor to collectthe human body's point cloud along with the body centroid, and (ii) avariational recurrent autoencoder (VRAE) to compute the anomaly level of thebody motion based on the acquired point cloud. A fall is claimed to haveoccurred when the spike in anomaly level and the drop in centroid height occursimultaneously. The mmWave radar sensor provides several advantages, such asprivacycompliance and high-sensitivity to motion, over the traditional sensingmodalities. However, (i) randomness in radar point cloud data and (ii)difficulties in fall collection/labeling in the traditional supervised falldetection approaches are the two main challenges. To overcome the randomness inradar data, the proposed VRAE uses variational inference, a probabilisticapproach rather than the traditional deterministic approach, to infer theposterior probability of the body's latent motion state at each frame, followedby a recurrent neural network (RNN) to learn the temporal features of themotion over multiple frames. Moreover, to circumvent the difficulties in falldata collection/labeling, the VRAE is built upon an autoencoder architecture ina semi-supervised approach, and trained on only normal activities of dailyliving (ADL) such that in the inference stage the VRAE will generate a spike inthe anomaly level once an abnormal motion, such as fall, occurs. During theexperiment, we implemented the VRAE along with two other baselines, and testedon the dataset collected in an apartment. The receiver operating characteristic(ROC) curve indicates that our proposed model outperforms the other twobaselines, and achieves 98% detection out of 50 falls at the expense of just 2false alarms.

mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder