Implementation and evaluation of a low-power sound-based user activity recognition system

The paper presents a prototype of a wearable, soundanalysis based, user activity recognition device. It focuses on low-power realization suitable for a miniaturized implementation. We describe a tradeoff analysis between recognition performance and computation complexity. Furthermore, we present the hardware prototype and the experimental evaluation of its recognition performance. This includes frame by frame recognition, event detection in a continuous data stream and the influence of background noise. 1

Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers

The paper presents a technique to automatically track the progress of maintenance or assembly tasks using body worn sensors. The technique is based on a novel way of combining data from accelerometers with simple frequency matching sound classifcation. This includes the intensity analysis of signals from microphones at different body locations to correlate environmental sounds with user activity. To evaluate our method we apply it to activities in a wood shop. On a simulated assembly task our system can successfully segment and identify most shop activities in a continuous data stream with zero false positives and 84.4% accuracy

Analysis of chewing sounds for dietary monitoring

Abstract. The paper reports the results of the first stage of our work on an automatic dietary monitoring system. The work is part of a large European project on using ubiquitous systems to support healthy lifestyle and cardiovascular disease prevention. We demonstrate that sound from the user’s mouth can be used to detect that he/she is eating. The paper also shows how different kinds of food can be recognized by analyzing chewing sounds. The sounds are acquired with a microphone located inside the ear canal. This is an unobtrusive location widely accepted in other applications (hearing aids, headsets). To validate our method we present experimental results containing 3500 seconds of chewing data from four subjects on four different food types typically found in a meal. Up to 99 % accuracy is achieved on eating recognition and between 80% to 100 % on food type classification.

Towards wearable autonomous microsystems, Pervasive Computing

Abstract. This paper presents our work towards a wearable autonomous microsystem for context recognition. The design process needs to take into account the properties ofa wearable environment in terms ofsensor placement for data extraction, energy harvesting, comfort and easy integration into clothes and accessories. We suggest to encapsulate the system in an embroidery or a button. The study ofa microsystem consisting ofa light sensor, a microphone, an accelerometer, a microprocessor and a RF transceiver shows that it is feasible to integrate such a system in a button-like form of 12 mm diameter and 4 mm thickness. We discuss packaging and assembly aspects ofsuch a system. Additionally, we argue that a solar cell on top ofthe button – together with a lithium polymer battery as energy storage – is capable to power the system even for a user who works predominantly indoors.

UWB for Noninvasive Wireless Body Area Networks: Channel Measurements and Results

The paper presents UWB channel measurements from 3 to 6 GHz for a body area network (BAN) in an anechoic chamber and an office room. Both, transmit and receive antenna were placed directly on the body. Channel parameters as delay spread and path loss are extracted from the measurements and the influence of the body is highlighted. We show that in some situations there are significant echoes from the body (e.g. from the arms) and we observed deterministic echoes from the floor that could help to simplify a RAKE receiver structure. Finally, we consider the overall energy consumption of the BAN and give decision regions for singlehop and multihop links in relation to signal processing energy

From Sensors to Miniature Networked SensorButtons

Wearable computing aims to empower people by providing intelligence embedded within garments. It relies on sensors placed at different locations of the body. To foster useracceptance sensors should be small, light, and unobtrusive. In this paper we present a wearable platform that addresses those challenges: a miniature networked SensorButton with the form factor of a button, so that it can be integrated in garments in an unobtrusive way. It has several sensors used in wearable computing, on-board processing power, a wireless link for sensor networking or communication with a base station, and it focuses on low power consumption. We describe its use to recognize user activity and highlight the need for further research in poweraware algorithms for wearable computing