Mobile health applications digital evidence taxonomy with knowledge sharing approach for digital forensics readiness

M-health is the current application that capable to monitor and detect human biological change and used the Internet as a platform to transfer and receive the data from the cloud providers. However, the advancement of Internet of Things (IoT) technology poses a great challenge for digital forensic experts in order to preserve, acquire and analyse digital evidence. Digital evidence taxonomy is one technique in digital forensics that facilitates digital forensics readiness and integration with knowledge sharing approach is necessary to allow digital forensics experts to share their knowledge. Therefore, this research was carried out that consists three phases, namely (1) initial phase, (2) intermediate phase and (3) final phase. In the initial phase, a systematic literature review was conducted to identify any potential gaps from the existing studies. Subsequently, digital evidence taxonomy in the IoT forensics layers was adopted, which consisted of three artefact categories to represent the IoT forensics layers. In the intermediate phase, 34 top rating m-health apps were used as a case study to validate the digital evidence taxonomy. From the analysis of the result, various types of information for forensic investigation were acquired, such as type of outdoor activity, activity timestamp, client IP address and date accessed. In the final phase, the M-Health Digital Evidence Taxonomy System (MDETS) was developed as a proof of concept to demonstrate the integration of digital evidence taxonomy with the knowledge-sharing approach to facilitate digital forensic readiness. Interviews were used as the instrument tool to evaluate knowledge sharing in terms of people, process and technology elements in enabling digital forensic readiness. The results from the interviews support that knowledge sharing facilitates digital forensic readiness in terms of people, process and technology elements. As a conclusion, the integration of digital evidence taxonomy with the knowledge-sharing approach gives the opportunity for the digital forensic community to enhance the existing approach or procedure to increase the findings of a digital forensic investigation and make digital forensic readiness more proactive within the organisation

Sensor de fotopletismografia por reflexão sem fios: projeto e desenvolvimento de hardware

Nos anos oitenta do último século começaram a surguir oxímetros wearable que se estabeleceram como um standart para a monitorização da saturação de oxigénio no sangue e actividade cardíaca, de forma não intrusiva. Os referidos oxímetros medem a percentagem de hemoglobina totalmente saturada com oxigénio (SPO2), transmitindo luz com comprimentos de onda diferentes, vermelha e infra-vermelha, através dos tecidos. Os dispositivos wearable atuais são frequentemente desenhados de forma modular, em que o módulo de medição e de display são integrados num único dispositivo. O armazenamento e tratamento de dados é difícil uma vez que são dispositivos de tamanho reduzido; baixo consumo de energia; baixo custo e baixa capacidade de processamento de dados. Tendo em conta que a quantidade de dados recolhidos é relativamente baixa, a sua transmissão de forma wireless é conveniente. Nesta dissertação é desenvolvido e testado um oxímetro de pulso em modo refletivo capaz de cálcular a saturação de oxigénio no sangue, o batimento cardíaco e enviar os dados de forma wireless para outros dispositivos. O hardware desenvolvido engloba quatro módulos funcionais: fonte de alimentação constituída por um conversor DC-DC e um regulador de tensão linear, circuito de carga e monitorização da bateria que controla os ciclos de carga e descarga da bateria, um módulo de rádio frequência que permite que o oxímetro comunique com outros dispositivos de forma wireless e um microcontrolador responsável por gerir todas as comunicações e pelo processamento de sinal. O sofware desenvolvido divide-se em duas partes: uma interface gráfica escrita em Matlab que permite a comunicação entre o computador e o oxímetro e o firmware do microcontrolador que engloba todos os algoritmos de cálculo do SPO2, do batimento cardíaco, drivers de periféricos, gestão das comunicações e aquisição e processamento dos dados.Ever since the early 80s from the last century, wearable oximeters appear as the established standard for non-invasive monitoring of arterial oxygen saturation (SpO2) and heart activity wearable oximeters can monitor arterial SpO2, which is the percentage of arterial hemoglobin that is fully saturated with oxygen, by transmitting red and infrared light through the finger, where it is sensed. The current wearable oximeters are frequently designed as single modular devices, namely, the measurement and display modules are integrated on a single device, which are responsible for several problems. Such devices lack effective data management functions and by being limited by size, power consumption and cost, advanced operating systems cannot be embedded to such wearable oximeters, making difficult to store and manage data. Bearing in mind that the amount of data pulse wave signal collected is small, transmit it wirelessly is convenient and effective. In this thesis a reflective pulse oximeter is developed and tested capable of assessing the oxygen blood saturation (SpO2), the heart rate and send the acquired data through wireless communication to other devices. The developed hardware comprises four functional modules: the power supply made of a DCDC converter and a linear voltage regulator, the charging circuit and battery monitoring system which controls the charging and discharging cycles of the battery, a radio-frequency module that allows the device to connect through wireless communication to other devices and a microcontroller responsible for the management of the communications and for the signal processing. The software developed in this thesis is made of two parts. One being the Matlab graphical interface that allows the communication between the oximeter and the PC while the other one being the microcontroller which comprises all the algorithms of SpO2, heart rate, management of the communication, drivers, and data acquisition and processing