RESUMO
The physiological and activity strain index (PASI) has been developed to improve the online decision support for workers exposed to heat stress. Fire fighters (smoke divers) which are exposed to both heat-stress and high-risk situations have been used as test case. PASI combines a modified version of the relatively well-known physiological strain index (PSI) with activity data from accelerometers. The algorithm has been developed based on tests in a laboratory, and it has been verified in two field tests performed by smoke divers exposed to heat stress. The verification demonstrates that it is possible to distinguish between high- and low-risk situations when data from accelerometers are added to the situation analysis. This indicates that PASI can contribute to an improved risk assessment and online decision support for smoke divers compared to using PSI alone.
Assuntos
Acelerometria/métodos , Sistemas de Apoio a Decisões Clínicas , Frequência Cardíaca/fisiologia , Transtornos de Estresse por Calor/diagnóstico , Monitorização Ambulatorial/métodos , Temperatura Cutânea/fisiologia , Acelerometria/instrumentação , Adulto , Algoritmos , Temperatura Corporal/fisiologia , Feminino , Bombeiros , Transtornos de Estresse por Calor/fisiopatologia , Humanos , Masculino , Modelos Biológicos , Monitorização Ambulatorial/instrumentação , Movimento/fisiologiaRESUMO
The cold and harsh climate in the High North represents a threat to safety and work performance. The aim of this study was to show that sensors integrated in clothing can provide information that can improve decision support for workers in cold climate without disturbing the user. Here, a wireless demonstrator consisting of a working jacket with integrated temperature, humidity and activity sensors has been developed. Preliminary results indicate that the demonstrator can provide easy accessible information about the thermal conditions at the site of the worker and local cooling effects of extremities. The demonstrator has the ability to distinguish between activity and rest, and enables implementation of more sophisticated sensor fusion algorithms to assess work load and pre-defined activities. This information can be used in an enhanced safety perspective as an improved tool to advice outdoor work control for workers in cold climate.