Validation of a method for the estimation of energy expenditure during physical activity using a mobile device accelerometer

The main goal of this paper consists on the adaption and validation of a method for the measurement of the energy expenditure during physical activities. Sensors available in a mobile device, e.g., a smartphone, a smartwatch, or others, allow the capture of several signals, which may be used to the estimation of the energy expenditure. The adaption consists in the comparison between the units of the data acquired by a tri-axial accelerometer and a mobile device accelerometer. The tests were performed by healthy people with ages between 12 and 50 years old that performed several activities, such as standing, gym (walking), climbing stairs, walking, jumping, running, playing tennis, and squatting, with a mobile device on the waist. The validation of the method showed that the energy expenditure is underestimated and super estimated in some cases, but with reliable results. The creation of a validated method for the measurement of energy expenditure during physical activities capable for the implementation in a mobile application is an important issue for increase the acceptance of the mobile applications in the market. As verified the results obtained are around 124.6 kcal/h, for walking activity, and 149.7 kcal/h, for running activity.This work was supported by FCT project PEst-OE/EEI/L A0008/2013 (Este trabalho foi suportado pelo projecto FCT PEst-OE/EEI/LA0008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303 – AAPELE – Architectures, Algorithms and Protocols for Enhanced Living Environments

ICT-based system to predict and prevent falls (iStoppFalls): results from an international multicenter randomized controlled trial

Background: Falls and fall-related injuries are a serious public health issue. Exercise programs can effectively reduce fall risk in older people. The iStoppFalls project developed an Information and Communication Technology-based system to deliver an unsupervised exercise program in older people’s homes. The primary aims of the iStoppFalls randomized controlled trial were to assess the feasibility (exercise adherence, acceptability and safety) of the intervention program and its effectiveness on common fall risk factors. Methods: A total of 153 community-dwelling people aged 65+ years took part in this international, multicentre, randomized controlled trial. Intervention group participants conducted the exercise program for 16 weeks, with a recommended duration of 120 min/week for balance exergames and 60 min/week for strength exercises. All intervention and control participants received educational material including advice on a healthy lifestyle and fall prevention. Assessments included physical and cognitive tests, and questionnaires for health, fear of falling, number of falls, quality of life and psychosocial outcomes. Results: The median total exercise duration was 11.7 h (IQR = 22.0) over the 16-week intervention period. There were no adverse events. Physiological fall risk (Physiological Profile Assessment, PPA) reduced significantly more in the intervention group compared to the control group (F1,127 = 4.54, p = 0.035). There was a significant three-way interaction for fall risk assessed by the PPA between the high-adherence (>90 min/week; n = 18, 25.4 %), low-adherence (n = 53, 74.6 %) and control group (F2,125 = 3.12, n = 75, p = 0.044). Post hoc analysis revealed a significantly larger effect in favour of the high-adherence group compared to the control group for fall risk (p = 0.031), postural sway (p = 0.046), stepping reaction time (p = 0.041), executive functioning (p = 0.044), and quality of life (p for trend = 0.052). Conclusions: The iStoppFalls exercise program reduced physiological fall risk in the study sample. Additional subgroup analyses revealed that intervention participants with better adherence also improved in postural sway, stepping reaction, and executive function

¹²³I-mIBG scintigraphy in neuroblastoma:development of a SIOPEN semi-quantitative reporting ,method by an international panel

PURPOSE: A robust method is required to standardise objective reporting of diagnostic (123)I-mIBG images in neuroblastoma. Prerequisites for an appropriate system are low inter- and intra-observer error and reproducibility across a broad disease spectrum. We present a new reporting method, developed and tested for SIOPEN by an international expert panel. METHOD: Patterns of abnormal skeletal (123)I-mIBG uptake were defined and assigned numerical scores [0–6] based on disease extent within 12 body segments. Uptake intensity was excluded from the analysis. Data sets from 82 patients were scored independently by six experienced specialists as unblinded pairs (pre- and post-induction chemotherapy) and in random order as a blinded study. Response was defined as ≥50 % reduction in post induction score compared with baseline. RESULTS: In total, 1968 image sets were reviewed individually. Response rates of 88 % and 82 % were recorded for patients with baseline skeletal scores ≤23 and 24-48 respectively, compared with 44 % response in patients with skeletal scores >48 (p = 0.02). Reducing the number of segments or extension scale had a small but statistically negative impact upon the number of responses detected. Intraclass correlation coefficients [ICCs] calculated for the unblinded and blinded study were 0.95 at diagnosis and 0.98 and 0.99 post-induction chemotherapy, respectively. CONCLUSIONS: The SIOPEN mIBG score method is reproducible across the full spectrum of disease in high risk neuroblastoma. Numerical assessment of skeletal disease extent avoids subjective evaluation of uptake intensity. This robust approach provides a reliable means with which to examine the role of 123I mIBG scintigraphy as a prognostic indicator in neuroblastoma

An Approach to Model-based Fault Detection in Industrial Measurement Systems with Application to Engine Test Benches.

An approach to fault detection (FD) in industrial measurement systems is proposed in this paper which includes an identification strategy for early detection of the appearance of a fault. This approach is model based, i.e. nominal models are used which represent the fault-free state of the on-line measured process. This approach is also suitable for off-line FD. The framework that combines FD with isolation and correction (FDIC) is outlined in this paper. The proposed approach is characterized by automatic threshold determination, ability to analyse local properties of the models, and aggregation of different fault detection statements. The nominal models are built using data-driven and hybrid approaches, combining first principle models with on-line data-driven techniques. At the same time the models are transparent and interpretable. This novel approach is then verified on a number of real and simulated data sets of car engine test benches (both gasoline—Alfa Romeo JTS, and diesel—Caterpillar). It is demonstrated that the approach can work effectively in real industrial measurement systems with data of large dimensions in both on-line and off-line modes