Estimating posture-recognition performance in sensing garments using geometric wrinkle modeling

A fundamental challenge limiting information quality obtained from smart sensing garments is the influence of textile movement relative to limbs. We present and validate a comprehensive modeling and simulation framework to predict recognition performance in casual loose-fitting garments. A statistical posture and wrinkle-modeling approach is introduced to simulate sensor orientation errors pertained to local garment wrinkles. A metric was derived to assess fitting, the body-garment mobility. We validated our approach by analyzing simulations of shoulder and elbow rehabilitation postures with respect to experimental data using actual casual garments. Results confirmed congruent performance trends with estimation errors below 4% for all study participants. Our approach allows to estimate the impact of fitting before implementing a garment and performing evaluation studies with it. These simulations revealed critical design parameters for garment prototyping, related to performed body posture, utilized sensing modalities, and garment fitting. We concluded that our modeling approach can substantially expedite design and development of smart garments through early-stage performance analysis

Psychophysiological body activation characteristics in daily routines

We present a novel approach to analyse and model psychophysiological body activation patterns that emerge from physical and mental activity during daily routines. We analyse our approach on a 62h dataset of daily routine recordings using acceleration and heart rate sensors. We present a descriptive analysis of psychophysiological activations during the routines using a novel visualisation technique. Our results show that daily routines exhibit different psychophysiological body activation characteristics. While physically-related routines are correlated with heart activity, mentally-related routines show activation patterns without physical activity. © 2009 IEEE

Influence of a loose-fitting sensing garment on posture recognition in rehabilitation

Several smart sensing garments have been proposed for postural and movement rehabilitation. Existing systems require a tight-fitting of the garment at body segments and precise sensor positioning. In this work, we analyzed errors of a loose-fitting sensing garment on the automatic recognition of 21 postures, relevant in shoulder and elbow-rehabilitation. The recognition performance of garment-attached acceleration sensors and additional skin-attached references was compared to discuss challenges in a garment-based classification of postures. The analysis was done with one fixed-size shirt worn by seven participants of varying body proportions. The classification accuracy using data from garment-integrated sensors was on average 13% lower compared to that of skin-attached reference sensors. This relation remained constant even after selecting an optimal input feature set. For garment-attached sensors, we observed that the loss in classification accuracy decreased, if the body dimension increased. Moreover, the alignment error of individual postures was analyzed, to identify movements and postures that are particularly affected by garment fitting aspects. Contrarily, we showed that 14 of the 21 rehabilitation-relevant postures result in a low sensor alignment error. We believe that these results indicate critical design aspects for the deployment of comfortable garments in movement rehabilitation and should be considered in garment and posture selection. © 2008 IEEE

Comment on "Non-invasive monitoring of chewing and swallowing for objective quantification of ingestive behavior"

The paper of Sazonov et al (2008 Physiol. Meas. 29 525–41) addresses the topic of on-body sensor-based measurement and analysis of food intake and eating behaviour. The authors rightly pinpoint a lack of solutions to estimate eating behaviour and energy intake in contrast to the active development of energy expenditure prediction tools. Unfortunately, Sazonov and colleagues have missed reviewing a considerable amount of published research in the field of ubiquitous and wearable computing. Moreover, it should be noted that objective measurement techniques exist for laboratory studies of chewing and swallowing that could have served for the validation of their work. This letter summarizes relevant related works and identifies refinements of the study methodology suggested by Sazonov et al. Food intake behaviour is very variable and hard to capture. Nevertheless, the approaches towards automatic dietary monitoring (ADM) cited in this letter confirm the broad potential for sensing and pattern recognition techniques. ADM could eventually supplement or replace intake diarie

Ambient, on-body, and implantable monitoring technologies to assess dietary behaviour

Self-reports are frequently used in coaching programmes on dietary behaviour since they provide information on time of food consumption, food types, and amounts in the temporal resolution of individual meal and snack intakes. However, accuracy of self-reports is influenced by to the respondent's motivation, memorising, and literate capabilities. The manual labour to complete reports cannot be sustained for several weeks and months, as it would be needed for adequate diet coaching. Computer-based solutions have been developed to reduce the respondent's effort in filling forms. More recently, sensor-based monitoring approaches were developed, referred to as Automatic Dietary Monitoring (ADM), which target to eliminate manual intake recording entirely. This chapter introduces a technology-oriented taxonomy of dietary behaviour assessments. Sensing and information technology concepts are reviewed that have been demonstrated, or are applicable for dietary behaviour assessment in monitoring programs and out-of-lab studies. The information provided by these monitoring technologies is categorised in four dietary monitoring dimensions: intake schedule, eating microstructure, meal composition and preparation, and consumed food amount

A wearable earpad sensor for chewing monitoring

Today dietary assessments require manual information sampling in paper or electronic questionnaires on food type and other diet-related details. Low accuracies of 50% are confirmed for self-reporting, which weakens diet coaching effectiveness and is a major limitation for today's diet programs. Automatic Dietary Monitoring (ADM) using ubiquitous sensors was proposed to alleviate this problem. In this work, we present implementation and analysis results of a novel acoustic earpad sensor device to capture air-conducted vibrations of food chewing. In contrast to previous works, our new device reduces ear occlusion compared to laboratory setups by using wearable earpad headphones. We investigate the sensing principle, perform a spectral sound analysis, and compare food classification performance to a classic lab-based sensor setup. We present novel food texture clustering results for 19 foods, spurring the understanding of food texture structure. In addition, we detail findings of a recent exhibition installation, were 375 food samples were analysed using the new sensor prototype

Adaptive activity spotting based on event rates

To date many activity spotting approaches are static: once the system is trained and deployed it does not change anymore. There are substantial shortcomings of this approach, specifically spotting performance is hampered when patterns or sensor noise level changes. In this work an unsupervised sensitivity adaptation mechanism is proposed for activity event spotting based on expected activity event rates. The expected event rate for activity spotting was derived from the generalisation metric used in information retrieval. To illustrate generalisation effects and depict relations of spotting performance and event rate, different event rates were simulated and their precision-recall spotting performance analysed. Subsequently, the sensitivity adaptation concept is presented and evaluated. For this purpose two large datasets from personal healthcare applications were considered to explore benefits and limitations of this adaptation approach: recognition of drinking motions from inertial sensors and chewing strokes from sound. Results showed up to 28% spotting performance increase for event rate adapted operation, confirming performance benefits for sensitivity adaptation. The approach will be most applicable in situations, where estimated event rate statistics show low variance and long monitoring durations allow effective sensitivity adaptations

An opportunistic activity-sensing approach to save energy in office buildings

In this work, we recognised office worker activities that are relevant for energy-related control of appliances and building systems using sensors that are commonly installed in new or refurbished office buildings. We considered desk-related activities and people count in office rooms, structured into desk- and room-cells. Recognition was performed using finite state machines (FSMs) and probabilistic layered hidden Markov models (LHMMs). We evaluated our approach in a real living-lab office, including three private and multi-person office rooms. As example devices, we used different ceiling-mounted PIR sensors based on the EnOcean platform and plug-in power meters. In at least five days of study data per office room, including reference sensor data and occupant annotations, we confirmed that activities can be recognised using these sensors. For computer and desk work, an overall recognition accuracy of 95% was achieved. People count was estimated at 87% and 78% for the best-performing two office rooms. We furthermore present building simulation results that compare different control strategies. Compared to modern BEMS, our results show that 21.9% and 19.5% of electrical energy can be saved for controls based on recognised desk activity and estimated people count, respectively. These results confirm the relevance of building energy management based on activity sensing

From backpacks to smartphones: past, present and future of wearable computers

Do smart phones render wearable computers obsolete? Where does the rise of the smart phone leave wearable computing research? We answer these questions by examining past, present, and future of wearable platform research