This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/RBME.2014.2346487, IEEE Reviews in Biomedical Engineering
1
RBME-00039-2013.R1
Feasibility of Energy-autonomous Wireless Micro-sensors for Biomedical Applications: Powering and Communication Farhad Goodarzy, Student Member, IEEE, Efstratios (Stan) Skafidas, Senior Member, IEEE, Simone Gambini, Member, IEEE
Abstract— In this paper biomedical related, wireless miniature devices such as implantable medical devices (IMD), Neural prostheses (NP), embedded neural systems and body area network (BAN) systems are investigated and categorized. The two main subsystems of such designs, the RF subsystem and the energy source subsystem are studied in detail. Different application classes are considered separately, focusing on their specific data-rate and size characteristics. Also the energy consumption of state-of-the-art communication practices is compared to the energy that can be generated by current energy scavenging devices, highlighting gaps and opportunities. The RF subsystem is classified and the suitable architecture for each category of applications is highlighted. Finally a new figure of merit suitable for wireless biomedical applications is introduced to measure the performance of these devices and assist the designer in selecting the proper system for the required application. This figure of merit can effectively fill the gap of a much required method for comparing different techniques in simulation stage before a final design is chosen for implementation. Index Terms—Neural prosthesis, Implantable biomedical devices, Body sensor networks, Radio transceivers, Energy harvesting, Energy storage, Figure of merit, I.
INTRODUCTION
R
ISING HEALTHCARE costs as a fraction of gross domestic product (GDP) are a concern in almost all developing countries, and drive technical innovation in healthcare. Technologies enabling patient monitoring outside of the hospital and during daily activities or delivering continuous therapeutic electrical-stimulation have the potential to drastically improve patient compliance, and potentially enable earlier detection of a number of conditions, leading to drastically lower costs. For this reason, miniaturized medical devices have become an increased focus of the electronics community in recent years. Examples include implantable medical devices (IMD), neural prostheses (NP), embedded
This work was supported in part by National ICT Australia (NICTA). F. Goodarzy is with the department of EEE at the University of Melbourne and National ICT Australia (NICTA), Melbourne, VIC, 3010 Australia. (Phone: 61-3-9035 3630; e-mail: [email protected]). S. Gambini is with the department of EEE at the University of Melbourne, Melbourne, VIC, 3010 Australia. (e-mail: [email protected]). E. Skafidas is with the department of EEE at the University of Melbourne, Melbourne, VIC, 3010 Australia. (e-mail: [email protected])
Figure 1. A typical biomedical/ neural embedded node
neural systems and body area network (BAN) systems. These devices will measure various bio-potentials and relay them in a wireless fashion to some form of base station or directly stimulate the central or peripheral nervous system to help alleviate the symptoms of a disease [1-6]. Many different biomarkers are of interest (brain activity, blood pressure, blood glucose level, etc.) and as a result, several different sensing platforms have been proposed in the literature. In order to efficiently synthesize and review this body of work, we adopt a high level model (Fig.1) made of four major subsystems: 1- Sensing subsystem which interfaces with the surrounding environment through sensors and actuators. 2-
Signal processing subsystem, which performs local computation on the data and encodes the information in a suitable mode for transmission.
3-
Communication subsystem, which transmits/receives information to other network nodes or to a base station.
4-
Energy subsystem, which gathers, converts and efficiently processes the energy required to operate the system.
It is known that the main hurdle to the realization of these devices is the scarcity of energy, especially when battery-less operation is targeted [2, 3, 7-9]. As discussed later in this work, miniaturized energy sources produce power of the order of tens of nano-watts.
1937-3333 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/RBME.2014.2346487, IEEE Reviews in Biomedical Engineering
2
RBME-00039-2013.R1
TABLE I. LIST OF WIDELY USED BIOMEDICAL IMPLANTS AND NEURAL EMBEDDED SYSTEMS Application BMI [10-13]
EEG/EMG/ECG [14-18] Hearing Aid [19-22]
Data Rate
Body Loss
Latency
Size
1-10Mbps (100Ch,15KS/ch, 10b/S) 74.4/72/576 Kbps (300/500/4000 S/ch, 12b/S, 24/12/12Ch) 200Kbps (16KS/ch, 12b)
5-10mm (skin, fat, gray matter)
1
RBME-00039-2013.R1
Feasibility of Energy-autonomous Wireless Micro-sensors for Biomedical Applications: Powering and Communication Farhad Goodarzy, Student Member, IEEE, Efstratios (Stan) Skafidas, Senior Member, IEEE, Simone Gambini, Member, IEEE
Abstract— In this paper biomedical related, wireless miniature devices such as implantable medical devices (IMD), Neural prostheses (NP), embedded neural systems and body area network (BAN) systems are investigated and categorized. The two main subsystems of such designs, the RF subsystem and the energy source subsystem are studied in detail. Different application classes are considered separately, focusing on their specific data-rate and size characteristics. Also the energy consumption of state-of-the-art communication practices is compared to the energy that can be generated by current energy scavenging devices, highlighting gaps and opportunities. The RF subsystem is classified and the suitable architecture for each category of applications is highlighted. Finally a new figure of merit suitable for wireless biomedical applications is introduced to measure the performance of these devices and assist the designer in selecting the proper system for the required application. This figure of merit can effectively fill the gap of a much required method for comparing different techniques in simulation stage before a final design is chosen for implementation. Index Terms—Neural prosthesis, Implantable biomedical devices, Body sensor networks, Radio transceivers, Energy harvesting, Energy storage, Figure of merit, I.
INTRODUCTION
R
ISING HEALTHCARE costs as a fraction of gross domestic product (GDP) are a concern in almost all developing countries, and drive technical innovation in healthcare. Technologies enabling patient monitoring outside of the hospital and during daily activities or delivering continuous therapeutic electrical-stimulation have the potential to drastically improve patient compliance, and potentially enable earlier detection of a number of conditions, leading to drastically lower costs. For this reason, miniaturized medical devices have become an increased focus of the electronics community in recent years. Examples include implantable medical devices (IMD), neural prostheses (NP), embedded
This work was supported in part by National ICT Australia (NICTA). F. Goodarzy is with the department of EEE at the University of Melbourne and National ICT Australia (NICTA), Melbourne, VIC, 3010 Australia. (Phone: 61-3-9035 3630; e-mail: [email protected]). S. Gambini is with the department of EEE at the University of Melbourne, Melbourne, VIC, 3010 Australia. (e-mail: [email protected]). E. Skafidas is with the department of EEE at the University of Melbourne, Melbourne, VIC, 3010 Australia. (e-mail: [email protected])
Figure 1. A typical biomedical/ neural embedded node
neural systems and body area network (BAN) systems. These devices will measure various bio-potentials and relay them in a wireless fashion to some form of base station or directly stimulate the central or peripheral nervous system to help alleviate the symptoms of a disease [1-6]. Many different biomarkers are of interest (brain activity, blood pressure, blood glucose level, etc.) and as a result, several different sensing platforms have been proposed in the literature. In order to efficiently synthesize and review this body of work, we adopt a high level model (Fig.1) made of four major subsystems: 1- Sensing subsystem which interfaces with the surrounding environment through sensors and actuators. 2-
Signal processing subsystem, which performs local computation on the data and encodes the information in a suitable mode for transmission.
3-
Communication subsystem, which transmits/receives information to other network nodes or to a base station.
4-
Energy subsystem, which gathers, converts and efficiently processes the energy required to operate the system.
It is known that the main hurdle to the realization of these devices is the scarcity of energy, especially when battery-less operation is targeted [2, 3, 7-9]. As discussed later in this work, miniaturized energy sources produce power of the order of tens of nano-watts.
1937-3333 (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/RBME.2014.2346487, IEEE Reviews in Biomedical Engineering
2
RBME-00039-2013.R1
TABLE I. LIST OF WIDELY USED BIOMEDICAL IMPLANTS AND NEURAL EMBEDDED SYSTEMS Application BMI [10-13]
EEG/EMG/ECG [14-18] Hearing Aid [19-22]
Data Rate
Body Loss
Latency
Size
1-10Mbps (100Ch,15KS/ch, 10b/S) 74.4/72/576 Kbps (300/500/4000 S/ch, 12b/S, 24/12/12Ch) 200Kbps (16KS/ch, 12b)
5-10mm (skin, fat, gray matter)
