We are pleased to announce the “Data Protection Regulator Roundtable” (October 7) at MediaNama’s PrivacyNama conference. Our speakers will discuss their experience in running a data protection commission, for our audience of stakeholders in India and across the world. We’ll be in conversation with Commissioner Adv Collen Weapond (South Africa) and Sharon Azarya (Israel). Renuka […]
We are pleased to announce the “Privacy and Competition” panel (October 7) at MediaNama’s PrivacyNama conference. Our speakers will share their views on the use of anonymised data for competitive purposes by big tech companies, for our audience of stakeholders in India and across the world. We’ll be in conversation with Deeksha Manchanda (Chandhiok & […]
We are pleased to announce the “Geopolitics, Data Segmentation and Cross Border Data Flows” panel (October 6) at MediaNama’s PrivacyNama conference, to understand issues around cross border data flows, the status of global conversations, and global priorities for countries, as well as challenges regarding data segmentation and national concerns. We’ll be in conversation with Arindrajit […]
We’re especially pleased to announce that Usha Ramanathan will be giving the keynote address (Oct 6) at MediaNama’s PrivacyNama conference, to discuss the fundamental and foundational principles of privacy. Session: Keynote Address Date: October 6, 2022 Time: 1:30 PM – 2:30 PM IST This is an invite-only event. Please register here to attend. About the Speaker Usha […]
The development of smart wearable devices has driven the rapid progress of activity recognition. However, existing activity recognition methods are still struggling to recognize single arm swings due to coarse-grained sensor data segmentation. Refined arm-swing-wise data segmentation is vital in some specific cases, such as the rehabilitation of disabled patients. In this paper, we propose a smartwatch-based arm-swing-wise data segmentation approach for human activity recognition, which converts original sensor signals into square-wave signals to detect the cut-off points of each arm swing. Particularly, our method can adaptively adjust the window size and step size of a sliding window without considering the change of swing speed. Empirical evaluation on two datasets, a self-collected dataset and a publicly-available benchmark dataset, shows superior performance of our approach over other methods under different settings, such as classifiers, features, and wearing positions.