Automatic heart rate normalization for accurate energy expenditure estimation. An analysis of activities of daily living and heart rate features.

INTRODUCTION: This article is part of the focus theme of Methods of Information in Medicine on "Pervasive Intelligent Technologies for Health". BACKGROUND: Energy Expenditure (EE) estimation algorithms using Heart Rate (HR) or a combination of accelerometer and HR data suffer from large error due to inter-person differences in the relation between HR and EE. We recently introduced a methodology to reduce inter-person differences by predicting a HR normalization parameter during low intensity Activities of Daily Living (ADLs). By using the HR normalization, EE estimation performance was improved, but conditions for performing the normalization automatically in daily life need further analysis. Sedentary lifestyle of many people in western societies urge for an in-depth analysis of the specific ADLs and HR features used to perform HR normalization, and their effects on EE estimation accuracy in participants with varying Physical Activity Levels (PALs). OBJECTIVES: To determine 1) which low intensity ADLs and HR features are necessary to accurately determine HR normalization parameters, 2) whether HR variability (HRV) during ADLs can improve accuracy of the estimation of HR normalization parameters, 3) whether HR normalization parameter estimation from different ADLs and HR features is affected by the participants' PAL, and 4) what is the impact of different ADLs and HR features used to predict HR normalization parameters on EE estimation accuracy. METHODS: We collected reference EE from indirect calorimetry, accelerometer and HR data using one single sensor placed on the chest from 36 participants while performing a wide set of activities. We derived HR normalization parameters from individual ADLs (lying, sedentary, walking at various speeds), as well as combinations of sedentary and walking activities. HR normalization parameters were used to normalized HR and estimate EE. RESULTS: From our analysis we derive that 1) HR normalization using resting activities alone does not reduce EE estimation error in participants with different reported PALs. 2) HRV features did not show any significant improvement in RMSE. 3) HR normalization parameter estimation was found to be biased in participants with different PALs when sedentary-only data was used for the estimation. 4) EE estimation error was not reduced when normalization was carried out using sedentary activities only. However, using data from walking at low speeds improved the results significantly (30-36%). CONCLUSION: HR normalization parameters able to reduce EE estimation error can be accurately estimated from low intensity ADLs, such as sedentary activities and walking at low speeds (3 - 4 km/h), regardless of reported PALs. However, sedentary activities alone, even when HRV features are used, are insufficient to estimate HR normalization parameters accurately.

Power optimization in body sensor networks: the case of an autonomous wireless EMG sensor powered by PV-cells.

Recent advances in ultra-low-power circuits and energy harvesters are making self-powered body sensor nodes a reality. Power optimization at the system and application level is crucial in achieving ultra-low-power consumption for the entire system. This paper reviews system-level power optimization techniques, and illustrates their impact on the case of autonomous wireless EMG monitoring. The resulting prototype, an Autonomous wireless EMG sensor power by PV-cells, is presented.

[Frequency of virus diseases in a regional hospital]. / Häufigkeit von Viruserkrankungen in einem Landkrankenhaus.

The district hospital Wertingen is nearly 100 km away from the Government-Investigation-Institute für Public Health, South-Bavaria, and there is no direct railway connection to Munich. This is a report how in spite of these insufficient traffic conditions in many cases the etiologic agent of virus diseases could be proved by isolation of virus or by showing specific IgM-antibodies or significant titer-rises in two blood-samples. For a successful investigation of the patient-material the knowledge of clinical symptoms and the anamnesis is most important. Furthermore is important the optimal time for collecting the material, which is for virus isolation always the earliest time after the onset of illness. For demonstration of titer-rises a second blood sample during the recovery has to be sent to the laboratory. During 1981 and 1982 in the district hospital Wertingen the diagnosis of 116 patients with virus diseases could be proved by demonstration of the etiologic agent.

[Yersinioses. Clinical, patho-anatomical and pathogenetic problems (author's transl)]. / Yersiniosen. Probleminfektionen aus klinischer, pathologisch-anatomischer und pathogenetischer Sicht.

On the basis of a selected patient group and bioptic material, the difficulties in diagnosing Y. pseudotuberculosis and Y. enterocolitica infections are presented from the clinical and morphological point of view, particularly when extraintestinal complications are present. Taking one patient with Y. enterocolitica infection and recurrent oligoarthritis as an example, the possible pathogenetic significance of circulating immune complexes for the development of extraintestinal symptoms is being discussed.

[Influenza A virus infection, a precipitating factor for the major heart attack (author's transl)]. / Influenza-A-Virus-Infektion, ein präzipitierender Faktor für den grossen Herzanfall?

A report on 5 men aged from 53 to 75 years whose clinical symptoms were characterized by a major attack. There was a striking temporal relationship between the onset of this and an influenza infection which, in these 5 patients with chronic heart diseases led to a severe, and in the youngest patient, even to a fatal course. In an acute heart attack it seems rational to take into consideration an acute influenza virus infection as a precipitating factor in existing chronic heart disease and to institute virological studies.