An IoT Based Architecture for Enhancing the Effectiveness of Prototype Medical Instruments Applied to Neurodegenerative Disease Diagnosis
1. Motivation and Introduction
- the proposal of a novel IoT-based architecture addressing requirements of prototype medical instruments, with the following characteristics: low-cost; based on well-accepted, secure, open and interoperable message oriented solutions; and able to include other information coming from ancillary sensors;
- the identification and selection of formal performance metrics for the characterization of the real-time behavior of the proposed architecture;
- the implementation of a testbed with both local and cloud servers, in order to provide close-to-reality results;
- an experimental measurement campaign for the characterization of the real-time behavior varying the location of the servers and the size of the exchanged information.
2. Early Neurodegenerative Disease Diagnosis
2.1. Transcranial Magnetic Stimulation for Early Neurodegenerative Disease Diagnosis
2.2. Diagnosis Protocol with Transcranical Magnetic Stimulation
- NMM: the number of distinct values of TISI that should be used in each cycle of the diagnosis phase associated with MM trains;
- a set of NMM TISI;
- NEM: the number of distinct values of TISI that should be used in each cycle of the diagnosis phase associated with EM trains;
- a set of NEM TISI;
- NREF: the number of reference stimuli, i.e., REF trains, that should be issued in each cycle of the diagnosis phase;
- TITI,MIN, TITI,MAX: the range of TITI to use in the diagnosis phase, to ensure aperiodic stimulation for optimal patient response;
- NC: the number of times that each cycle should be repeated in the diagnosis phase.
2.3. Advantages Offered by Cloud Services
3. The Proposed Cloud Architecture
3.1. The MQTT and AMQP Protocols
3.2. The Proposed Architecture
3.3. The Proposed Metrics for Performance Evaluation
- T1: this timestamp is assigned by the instruments when a new set of data is ready to be sent to the architecture.
- T2: this timestamp is assigned by the architecture when a new set of data arrives to the Web Server coming from the instrument.
- T3: this timestamp is assigned by the database when a new set of data coming from the instruments is permanently stored in its data table.
- T4: this timestamp is assigned by the Client when a new set of data arrives through the websocket.
- T5: this timestamp is assigned by the Client when a new command leaves the Client addressed to the architecture.
- T6: this timestamp is assigned by the architecture when a new set of data arrives to the Web Server coming from the Client.
- T7: this timestamp is assigned by the database when a new set of data coming from the Client is permanently stored in its data table.
- T8: this timestamp is assigned by the instrument when a command arrives through the architecture.
- DIC = T4–T1: the overall end-to-end delay from the TMS instrument to the remote Client.
- DIS = T2–T1: the end-to-end delay from the TMS instrument to the Web Server.
- DID = T3–T1: the end-to-end delay from the TMS instruments to the Database.
- DCI = T8–T5: the reverse overall end-to-end delay from the remote Client to the TMS instrument.
- DCS = T6–T5: the end-to end delay from the Client to the Web Server.
- DCD = T7–T5: the end-to-end delay from the Client to the Database.
4. Validation and Experimental Results
4.1. The Prototype Medical Instrument for Neurodegenerative Disease Diagnosis
- protocol invariant data, as the amplitude of the stimuli, AmTS, AmCS, AeCS; each of which is one byte wide;
- an array of NTOT records, each of which specifying the stimulus type (1 byte), the TISI value (1 byte), the TITI value (1 byte), and the calculated MEP value (2 byte).
4.2. Experimental Setup
- The TMS Instrument is based on a Raspberry Pi 2 single board computer, as described in Section 4.1. The raspberry Pi 2 hosts a quad-core 64 bit ARM processor (clock frequency 1.2 GHz), 1 GB of RAM, four USB ports, a LAN interface and HDMI video connection; it runs the Linux-based Raspbian OS. The TMS Instrument role is to initiate the experiment, sending the first data message. It also terminate the experiment, receiving the Client command. A custom transport layer abstracting the communication libraries for both AMQP and MQTT has been implemented in Python; in particular, the transport layer uses the Paho library for MQTT and the Pika library for AMQP.
- The Local Server is a machine specifically created for the experiments. It is located inside the domain of the University of Brescia. It is a VMware (ESXi 6.5) virtual machine (VM); the VM is hosted on a DELL PowerEdge R630 (Intel Xeon E5–2 core, 128 GB RAM, 4 × 1 Gbps Ethernet), one of the facility available in the University of Brescia eLUX laboratory . The VM has a single CPU, 2 GB of RAM and has CentOS7 as guest operating System (OS). It hosts the RabbitMQ AMQP broker, with the HTTP management plugin enabled, and the Mosquitto MQTT broker. The local server hosts also the websocket server, which is a simple Python 3.6 program. It uses the Tornado web server websocket implementation to communicate with the Client and it runs the same custom transport layer used in the TMS Instrument (which, as previously stated, leverages the Paho library for MQTT and the Pika library for AMQP).
- The Cloud Server is an instance of the Amazon AWS EC2 t2.micro virtual machine with a single CPU and 1 GB of RAM. The Cloud Server is hosted by the US East (Ohio) Amazon Web Service (AWS) data center and it has been specifically created for the experiments. The software configuration of the Cloud Server is identical to the Local Server one; a minimal effort is required for migration from Local to cloud Server.
- The Database Server is located inside the domain of the University of Brescia. It is a VMware (ESXi 6.5) VM; it is hosted on a Syneto Ultra 205 hyperconverged system (Intel Xeon E5–6-core, 64 GB RAM, 4 × 1 Gbps Ethernet), another facility available in the eLUX laboratory. The VM has 4 CPU, 16 GB RAM and has CentOS7 as guest OS. It hosts the different databases (including MariaDB, Influxdb, PosgreSQL) that collect the laboratory data. For the experiments of this work, the Influxdb, an open-source database purposely-designed for time series storage, has been chosen.
4.3. Estimation of Measurement Uncertainty
4.4. Experimental Results
- interval between data transfer (1 s or 10 s);
- location of Broker and Web Server (Local Server or Cloud Server);
- the used message protocol (AMQP or MQTT).
5. Conclusions and Future Development
Conflicts of Interest
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|Metric||Device A||Device B|
|Local||TMS to Client||TMS Instrument||Client||3.3|
|TMS to Server||TMS Instrument||Local Server||1.2|
|TMS to Database||TMS Instrument||DB Server||1.3|
|Client to TMS||Client||TMS Instrument||3.3|
|Client to Server||Client||Local Server||3.2|
|Client to Database||Client||DB Server||3.2|
|Cloud||TMS to Client||TMS Instrument||Client||3.3|
|TMS to Server||TMS Instrument||AWS Server||1.3|
|TMS to Database||TMS Instrument||DB Server||1.3|
|Client to TMS||Client||TMS Instrument||3.3|
|Client to Server||Client||Cloud Server||3.2|
|Client to Database||Client||DB Server||3.2|
|Metric||MQTT @ 1 s||AMQP @ 1 s||MQTT @ 10 s||AMQP @ 10 s|
|Mean||95 perc.||Mean||95 perc.||Mean||95 perc.||Mean||95 perc.|
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Depari, A.; Fernandes Carvalho, D.; Bellagente, P.; Ferrari, P.; Sisinni, E.; Flammini, A.; Padovani, A. An IoT Based Architecture for Enhancing the Effectiveness of Prototype Medical Instruments Applied to Neurodegenerative Disease Diagnosis. Sensors 2019, 19, 1564. https://doi.org/10.3390/s19071564
Depari A, Fernandes Carvalho D, Bellagente P, Ferrari P, Sisinni E, Flammini A, Padovani A. An IoT Based Architecture for Enhancing the Effectiveness of Prototype Medical Instruments Applied to Neurodegenerative Disease Diagnosis. Sensors. 2019; 19(7):1564. https://doi.org/10.3390/s19071564Chicago/Turabian Style
Depari, Alessandro, Dhiego Fernandes Carvalho, Paolo Bellagente, Paolo Ferrari, Emiliano Sisinni, Alessandra Flammini, and Alessandro Padovani. 2019. "An IoT Based Architecture for Enhancing the Effectiveness of Prototype Medical Instruments Applied to Neurodegenerative Disease Diagnosis" Sensors 19, no. 7: 1564. https://doi.org/10.3390/s19071564