Upplevelsen av att vistas på offentliga platser i större städer är olika. De plats er som upplevs obehagliga och med oro för att utsättas för brott är inte alltid de platser där det statistiskt sätt sker flest brott. Stockholm är en av de städer som jag har tittat på och på vilka platser i storstaden som upplevs på vilket sätt. Risken för att utsättas för brott på offentliga platser har ökat och våldsbrott på offentliga platser står för 18 % av antalet anmälda brott och där ingår även misshandel och sexualbrott. Jag har titta närmare på hur man upplever sin trygghet i vardagen och på vilket sätt man kan hindra eller förändra de utsattas beteenden och beslut. Mitt arbete har lett fram till en produkt som är personbunden och har som uppgift att visa anhöriga vart man befinner sig och hur man mår för att snabbt kunna ta kontakt med någon om otryggheten ökar och förstärka upplevelsen av kontroll och trygghet. Det resulterade i ett armbandskoncept som har en tjänst kopplat i sig i form av en applikation.

Purpose. To investigate the validity of the Sense Wear Pro Armband (SWA) to measure energy expenditure (EE) in healthy participants using wheelchair ergometry as an exercise modality. Method. Minute by minute EE was measured simultaneously using the SWA and indirect calorimetry(IC) during three different wheeling speeds including self-selected speed (0.81 m/s), moderate speed (1.11 m/s), and fast speed (1.73 m/s). Results. Twenty healthy volunteers (age = 34.0 (5.8) years)participated. The intraclass correlation coefficients (ICCs) were 0.50 (p=0.010), 0.59 (p=0.003), and 0.68 (p=0.000) for the self-selected speed, moderate speed, and fast speed wheeling, respectively. The SWA overestimated EE 57.8%, 57.4 %, and 63.7% for self-selected speed, moderate speed, and fast speed, respectively. Conclusions. The SWA failed to provide an accurate estimate of EE as measured by indirect calorimetry for wheelchair ergometry exercise in healthy subjects. The SWA overestimated EE for all exercise intensities. / REHABILITATION SCIENCE-PHYSICAL THERAPY

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Thesis (M.Ed.)--Cleveland State University, 2008. / Abstract. Title from PDF t.p. (viewed on July 7, 2008). Includes bibliographical references (p. 33-36). Available online via the OhioLINK ETD Center. Also available in print.

This study examined the reliability and validity of the SenseWear Armband (SWA, BodyMedia, Inc.) during rest and exercise compared to indirect calorimetry (IC). Energy expenditure (EE) was assessed with SWA and IC in 13 males during two resting and one cycle ergometry (40 min at 60% VO_2peak) sessions. In a second experiment, 20 adults walked on a treadmill for 30 min at 3 intensities while IC and SWA measured EE. At rest, no significant differences were found between EE measurements from the SWA (1.3 ± 0.1 kcal/min) and IC (1.3 ± 0.1 kcal/min), and the methods were significantly correlated (r = 0.76). The SWA EE estimation was reliable when comparing the two resting visits (r = 0.93). For the ergometer protocol, no significant differences were found between the SWA and IC measurements of EE early, mid, or late in exercise or for the total bout, although the measurements were not correlated (r = 0.03-0.12). The SWA EE estimate of walking increased with treadmill speed but not with inclination. The SWA significantly overestimated the EE of walking with no grade (27.4% for 3mph; 12.6% for 4mph) and significantly underestimated EE on the 5% grade (21.9%) (p<0.02). The SWA estimation of EE correlated with IC (r = 0.47-0.69). The SWA provided valid and reliable estimates of EE at rest. The SWA provided similar mean estimates of EE as IC on the ergometer, however the individual error was large. The SWA overestimated the EE of flat walking and underestimated inclined walking EE. / Master of Science

abstract: Dietary protein is known to increase postprandial thermogenesis more so than carbohydrates or fats, probably related to the fact that amino acids have no immediate form of storage in the body and can become toxic if not readily incorporated into body tissues or excreted. It is also well documented that subjects report greater satiety on high- versus low-protein diets and that subject compliance tends to be greater on high-protein diets, thus contributing to their popularity. What is not as well known is how a high-protein diet affects resting metabolic rate over time, and what is even less well known is if resting metabolic rate changes significantly when a person consuming an omnivorous diet suddenly adopts a vegetarian one. This pilot study sought to determine whether subjects adopting a vegetarian diet would report decreased satiety or demonstrate a decreased metabolic rate due to a change in protein intake and possible increase in carbohydrates. Further, this study sought to validate a new device called the SenseWear Armband (SWA) to determine if it might be sensitive enough to detect subtle changes in metabolic rate related to diet. Subjects were tested twice on all variables, at baseline and post-test. Independent and related samples tests revealed no significant differences between or within groups for any variable at any time point in the study. The SWA had a strong positive correlation to the Oxycon Mobile metabolic cart but due to a lack of change in metabolic rate, its sensitivity was undetermined. These data do not support the theory that adopting a vegetarian diet results in a long-term change in metabolic rate. / Dissertation/Thesis / M.S. Nutrition 2012

The high prevalence of work-related musculoskeletal disorders in occupational settings necessitates the development of economic, accurate, and convenient methods for quantifying biomechanical risk exposures. In terms of lifting, the occupational work environment does not provide resources for recording the start and end times of lifting tasks performed by individual workers. As a result, automatic detection of lift starts and ends is required for practical purposes. Occupational lifting styles vary depending on the asymmetry angle, which is the degree of shoulder or trunk rotation required by the lifting task. Predictive or machine learning (ML) algorithms have been increasingly used in the ergonomics field to identify occupational risk factors, such as lifting loads. However, such algorithms are often developed and validated using the dataset collected from the same lab-based experimental set-up, which limits their external validity. The recent development of wearable armbands with surface electromyography (sEMG) electrodes provides a low-cost, wireless, and non-invasive way to collect EMG data beyond laboratory settings. Despite their tremendous potential for field-based workload estimation, these armbands have not been widely implemented yet in automated lift detection and occupational workload estimation. The objective of this study was to evaluate the performance of machine learning (ML) algorithms in the automatic detection of lifts and classification of hand loads during manual lifting tasks from the data acquired by a wearable armband sensor with eight surface electromyography (sEMG) electrodes. Twelve healthy participants (six male and six female) performed repetitive symmetric (S), asymmetric (A), and free dynamic (F) lifts with three different hand-load levels (5 lb, 10 lb and 15 lb) at two origin (24" and 36") and two destination heights (6" and 36"). Three ML algorithms were utilized: Random Forest (RF), Support Vector Machines (SVM) and Gaussian Naïve Bayes (GNB). For lift detection, a subset of four participants was analyzed as a preliminary investigation. RF showed the best performance with the mean start and end errors of 0.53 ± 0.25 seconds and 0.76 ± 0.28 seconds, respectively. The accuracy score of 84.3 ± 3.3% was reported for lift start and 83.3 ± 9.9% for lift end. For hand-load classification, prediction models were developed using four different lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8 ± 7.3%) compared to A (83.3 ± 7.2%), S+A (82.1 ± 7.3%), and F (83.4 ± 8.1%). Overall, findings indicate that the implementation of ML algorithms with wearable EMG armbands for automatic lift detection in occupational settings can be promising. In hand-load classification, models developed with only controlled symmetric lifts were less accurate in predicting loads of more dynamic, unconstrained lifts, which is common in real-world settings. However, since both A and S+A demonstrated equivalent model accuracy with F, EMG armbands possess strong potential for estimating the hand loads of free-dynamic lifts using constrained lift trials involving asymmetric lifts. / Master of Science / Naturalistic occupational settings involve prolonged, frequent, and physically heavy lifting-lowering tasks that are associated with a high prevalence of musculoskeletal disorders. This necessitates the development of economic, accurate, and convenient methods for quantifying risk exposures such as load magnitude, repetitiveness and duration. In terms of lifting, the occupational work environment does not provide resources for recording the start and end of lifting tasks performed by individual workers for analysis. As a result, automatic detection of lift starts and ends is required for practical purposes. Occupational lifting styles vary depending on the asymmetry angle, which is the degree of shoulder or trunk rotation required by the lifting task. Predictive or machine learning (ML) algorithms have been increasingly used in the ergonomics field to identify occupational risk factors, such as lifting loads. However, such algorithms are often developed and validated using the dataset collected from the same lab-based experimental set-up, which limits their external validity. Electromyographic (EMG) signals representing the neuromuscular activity associated with muscular contractions can be valuable for exposure assessment. The recent development of wearable armbands with surface electromyography (sEMG) electrodes provides a low-cost, wireless, and non-invasive way to collect EMG data beyond laboratory settings. Despite their tremendous potential for field-based workload estimation, these armbands have not been widely implemented yet in automated lift detection and occupational workload estimation. The objective of this study was to evaluate the performance of machine learning (ML) algorithms in the automatic detection of lifts and classification of hand loads during manual lifting tasks from the data acquired by a wearable armband sensor with eight surface electromyography (sEMG) electrodes. Twelve healthy participants (six male and six female) performed repetitive symmetric (S), asymmetric (A), and free dynamic (F) lifts with three different hand-load levels (5 lb, 10 lb and 15 lb) at two origin (24" and 36") and two destination heights (6" and 36"). Three ML algorithms were utilized: Random Forest (RF), Support Vector Machines (SVM) and Gaussian Naïve Bayes (GNB). For lift detection, a subset of four participants was analyzed as a preliminary investigation. RF showed the best performance with the mean start and end errors of 0.53 ± 0.25 seconds and 0.76 ± 0.28 seconds, respectively. The accuracy score of 84.3 ± 3.3% was reported for lift start and 83.3 ± 9.9% for lift end. For hand-load classification, prediction models were developed using four different lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8 ± 7.3%) compared to A (83.3 ± 7.2%), S+A (82.1 ± 7.3%), and F (83.4 ± 8.1%). Overall, findings indicate that the implementation of ML algorithms with wearable EMG armbands for automatic lift detection in occupational settings can be promising. In hand-load classification, models developed with only controlled symmetric lifts were less accurate in predicting loads of more dynamic, unconstrained lifts, which is common in real-world settings. However, since both A and S+A demonstrated equivalent model accuracy with F, EMG armbands possess strong potential for estimating the hand loads of free-dynamic lifts using constrained lift trials involving asymmetric lifts.

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No description available.

Titel: Omedveten arousal i butiksmiljön. Nyckelord: Arousal, omedveten arousal, servicescape, butiksmiljö, konsumtionsbeteende, GSR-armband, eyetracking, elektrodermal aktivitet. Syfte: Syftet med denna studie är att undersöka vilka faktorer som framkallar omedveten arousal i en riktig butiksmiljö. Vidare vill vi undersöka om skillnader i kundrelaterade konsumtionsvariabler påverkar omedveten arousal. Metod: Studien kombinerar en kvantitativ och kvalitativ ansats där 60 respondenter deltog i studien. Data samlades in via två enkäter, GSR-armband och ett par eyetracking-glasögon. Bidrag: Studien har bidragit med en kategorisering av omedvetna arousalutslag baserat på vad respondenter reagerat på i butiken. Skillnader i kundrelaterade konsumtionsvariabler har identifierats i förhållande till omedveten arousal. Originalitet: Med hjälp av studiedesignen har data kunnat samlas in i en riktig butiksmiljö. Respondenterna har således kunnat handla som vanligt i en miljö med andra kunder, personal och fysiska faktorer i butiken. / Title: Unconscious arousal in a store environment. Key words: Arousal, unconscious arousal, servicescape, store environment, consumer behavior, GSR-wristband, eye tracking, electrodermal activity. Purpose: The purpose with this study is to examine what factors evoke unconscious arousal in a real store environment. We will also examine if there are any differences in customer related consumption variables which affect unconscious arousal. Method: The study combines a quantitative and a qualitative approach where 60 respondents participated in the study. Data was collected by two surveys, a GSR-wristband and a pair of eyetracking-glasses. Contributions: The study has contributed with a categorization of unconscious arousal peaks based on what the respondents reacted to in the store. Differences in customer related consumption variables have been identified in relation to unconscious arousal. Originality: The study design has allowed data to be collected in a real store environment. The respondents have been able to shop as usual in an environment with other customers, staff and physical factors in the store.