biosignals for everyone · computing to the physiological domain, changing the way in which...

64 PERVASIVE computing Published by the IEEE CS n 1536-1268/14/$31.00 © 2014 IEEE

Biosignals for Everyone

T oolkits are an important asset: they help researchers and engi-neers advance in their field of work without having to deepen their expertise in somewhat

peripheral knowledge domains or reinvent research techniques with the technology at hand. Software has a long tradition of provid-ing such facilities, examples of which include the Java Swing GUI widget toolkit and the scikit-learn software packages. Hardware is now fol-lowing a similar path, with low-cost toolkits

driving innovation in ways never before seen.

Almost a decade and thousands of assembled units after its debut, the Arduino platform has become the centerpiece of any maker or tinkerer’s toolbox. As its website explains, “Arduino is an open source electronics

platform based on easy-to-use hardware and software. It’s intended for anyone making interactive projects.” Since its humble beginnings in 2005 as the brain child of Massimo Banzi and his team,1 researchers at the MIT Media Lab have made it clothing-compatible,2 and it has become the de facto accessory development kit for Google’s Android OS.

Do-it-yourself hardware platforms have grown in their own right, fostering the development of interactive systems that bridge the analog and digital worlds in what is broadly defined as physical computing.3 So far, physical computing has been characterized by the use of sensors and actuators designed to

deal with very simple requirements in terms of signal acquisition setup, such as the need for a relatively high tolerance to noise or for low sampling rates.

Physiological computing, on the other hand, poses several different challenges related to the more complicated requirements of physiological data acquisition (for example, the need for higher signal-to-noise ratios or greater accuracy in the sampling rate). To address these challenges, we present a novel development platform especially designed to consider the requirements of physiological data acquisition. Furthermore, our platform makes biosignals readily available to anyone interested in exploring the field and provides a framework that we hope can drive a new wave of research and projects within the global research and engineering community.

Let’s Get Physi…ologicalBiosignals have been used in the healthcare and medical domains for more than 100 years, the best-known examples being electrocardiography (ECG) and electroencephalography (EEG) signals. The application of engineering principles and devices in the field has proven to be of paramount importance, leading to remarkable technical, methodological, and scientific achievements.4 Today, biosignals are an increasingly popular research topic within the global engineering community: their potential applications far extend the medical arena, paving the way for the nascent field of physiological computing.5

Physiological computing can be generally defined as the study and development of interactive software and hardware systems

A novel development platform extends the principles of physical computing to the physiological domain, changing the way in which projects and applications involving physiological data can be made—welcome news for those who prefer a do-it-yourself approach.

Hugo Plácido da Silva, Ana Fred, and Raúl MartinsUniversity of Lisbon

F E a t u r E : B i o m E d i c a L E n G i n E E r i n G

octobEr–dEcEmbEr 2014 PERVASIVE computing 65

capable of sensing, processing, reacting, and interfacing the digital and analog worlds. The difference between it and physical computing is the fact that physiological computing focuses specifically on the use of biosignals, which opens up a whole separate class of problems. Although biomedical engineering is a related classical discipline, today biosignals drive hobbyists, students, engineers, and other user groups in fields including computer science, informatics, and electrical engineering.

Modern physiological computing applications include mechanical engineering for human performance enhancement using exoskeletons,6 electrical engineering for health sensing and telemedicine,7 and computer science for HCI,8 among others.9–11 Health sensing alone is extremely important—for example, periodic biosignal monitoring enables early-stage discovery and management of problems such as heart attacks or stroke before they occur,4,7 and real-time assessment can be fundamental for things like muscle-skeletal rehabilitation or injury prevention.

More recently, the HCI and pervasive computing communities have started using biosignals for a variety of applications, such as demonstrating the feasibility of muscle-computer interfaces with sensors mounted on the forearm,12 and exploring the use of sympathetic nervous system activity and motion for stress recognition in a mobile environment.13

anatomy of a BitalinoPhysical computing researchers have the Arduino and its successors and predecessors, but the physiological computing community lacks a comparable tool. Biosignals have specific requirements for which typical physical computing platforms aren’t particularly tuned, and many projects end up heavily bounded by the high cost and limited access to suitable

hardware materials. Building on the guiding principles of existing physical computing hardware platforms, we created BITalino, a highly versatile toolkit designed to make biosignals available for anyone interested in innovative and creative engineering in a physiological computing framework (www.bitalino.com). Figure 1 depicts

the BITalino development platform, and Table 1 summarizes its primary specifications.

The hardware consists of a low-cost, modular wireless biosignal acquisition system, with a credit card-sized form factor that integrates multiple measurement sensors for bioelectrical and biomechanical data

(a)

(b)

(c)

Figure 1. BITalino biosignal acquisition hardware in its different configurations: (a) board, (b) plugged, (c) freestyle.

66 PERVASIVE computing www.computer.org/pervasive

FEaturE: BiomEdicaL EnGinEErinG

acquisition. The digital back end is supported by a control block based on the ATmega328P microcontroller, a power management block, and a communication block that uses a Class II Bluetooth v2.0 module for wireless data transfer; two auxiliary connectivity blocks enable RJ22 plugs to be added to the device. The analog front end integrates individual sensor blocks for electromyography (EMG), electrocardiography (ECG), electrodermal activity (EDA), and accelerometry (ACC); the board is also fitted with light-sensing (LUX) and LED blocks, to enable synchronization with third-party equipment such as a computer screen or video camera.

BITalino is currently provided as a kit that includes all the basic components anyone would need to enter the world of biosignals—namely, the previously described hardware blocks; a 550 mAh rechargeable LiPo battery; a two-lead electrode cable assembly (for ECG and EDA); a three-lead electrode cable assembly (for ECG and EMG); and a pack of five multipurpose pre-gelled Ag/AgCl electrodes that can be used for ECG, EMG, or EDA data acquisition.

In a worst-case power consump-tion scenario where all the sen-sors and LEDs are simultaneously

connected and using a 1,000-Hz sampling rate, BITalino uses around 65 mAh (approximately 60 percent from the Bluetooth module alone and approximately 15 percent from the LED). If just one or two sensors are used, an average of approxi-mately 50 mAh can be achieved, enabling BITalino to run continu-ously for close to 10 hours using the standard battery provided in the kit. BITalino’s modular design enables the battery to be easily swapped for a larger or smaller one, depending on the given use case. Considering that 2-Ah compact batteries (0.25 × 2.1 × 2.4”, and 36 g) are readily available, a user can achieve more than 40 hours of battery life in con-tinuous operation.

one Board, many optionsBy default, the system comes as a single board, with onboard sensors preconnected to analog and digital ports on the control block. However, the control, power, and communication blocks, as well as the firmware, are completely general purpose, enabling people to use BITalino’s digital back end with their own custom sensor and actuator designs. The BITalino platform’s versatility even extends to the point where each individual block can be physically detached from the main board, allowing people to use it

in many different ways, typically in the following three configurations:

• board, or without modifications, so people can simply experiment with the onboard sensors to support exper-imental activities or illustrate theoret-ical concepts through the real-time observation of underlying physiologi-cal phenomena (Figure 1a);

• plugged, where the analog front end is separated from the BITalino main board, leaving only the control, power, communication, and auxil-iary connectivity blocks so that peo-ple can interchangeably use different sensor combinations, connecting the sensor by cable (Figure 1b); and

• freestyle, where all the individual dig-ital and analog blocks are detached from the BITalino main board, en-abling people to combine them in any way that best suits their project ideas and applications (Figure 1c).

We also designed the analog front end to enable BITalino to be a broad-spectrum development platform for experimentation and rapid prototyping based on biosignals. The onboard sensors enable anyone to easily explore and work with the following:

• ECG, which is useful for one-lead measurement of the heart’s bioelec-trical activity and derived parameters (heart rate, heart rate variability, and so on) and can be applied in any stan-dard location (such as the chest, left/right hand palms, or left/right fingers). It’s important to highlight that this particular sensor integrates previous work by our group,14 which is why it can either work in a standard three- (+, –, and ground) or two-electrode configuration (in which a “virtual” ground is used).

•EMG, which is useful for measur-ing muscular bioelectrical activity and derived parameters such as on-set and duration. This sensor can be applied to any surface muscle and the output data used as control

TaBle 1 BITalino’s specifications.

Specifications

Sampling rate configurable to 1, 10, 100, or 1,000 Hz

Analog ports 4 input (10 bit) + 2 input (6 bit)

digital ports 4 input (1 bit) + 4 output (1 bit)

data link class II bluetooth v2.0 (~10-m range)

Actuators LEd

Sensors EmG, EcG, EdA, Acc, and LUX

Weight 30 g

Size 100 × 60 mm

battery 3.7-V LiPo

consumption ~65 mAh (with all peripherals active)


signals in biomechatronics or HCI applications.12

•EDA, which is useful for measuring skin resistance. A typical use case is to assess sympathetic nervous sys-tem activity with two sensor leads applied to the palms or feet, allow-ing the measurement of emotional arousal situations associated with variations in skin impedance caused by increased sweat secretion.15

•ACC, which is useful for measuring biomechanical events such as tilt, step counting, fall detection, and physical activity.

•LUX, which is useful for measur-ing ambient luminosity or for opti-cal synchronization with external sources (a typical example is the syn-chronization of BITalino with con-tent presented on a computer screen).

Table 2 presents some of the specifications of the individual sensor blocks provided by default on the analog front end. All sensors have an analog output, this being the primary connection between any sensor and the microcontroller unit (MCU) block, regardless of whether it’s an onboard, third-party, or custom-designed sensor. Together with the general-purpose firmware, this makes it possible for users to easily interface with virtually any sensor. We also designed BITalino so that users can connect multiple sensors of the same type in parallel,

a common requirement for, say, acquiring several EMG channels at the same time.

designed with Everyone in mindWe’ve put considerable effort into designing something that has both flexibility and ease of use. One of the largest barriers to using physiological sensors such as EMG, ECG, and EDA is connecting the cables between the electrodes and the analog front end. BITalino eases this burden to the point where the user just places the electrodes on the body and snaps the connectors into place. Figure 2 shows the physical interface between a user’s body and the BITalino board (in this case, for EMG and EDA).

As we previously described, the default approach for data transfer is the Bluetooth interface; given that one of our goals was to keep costs as low as possible, the standard Bluetooth module only supports the Serial Port Profile (SPP). However, the MCU block fully exposes the universal asynchronous receiver/transmitter (UART) pins, letting users replace the standard module with a more advanced one or to use another interface that they find more suitable for their application. With this design feature, users can even interface embedded systems (such as Raspberry Pi) directly with BITalino through the UART port.

Another important aspect that we focused on is safety. When building electronics that directly interface with the human body—particularly, sensors that require low impedance electrodes—it’s extremely important to ensure that the user is protected from electrical hazards.11,16 Electrodes should facilitate electrical outflows from the body to the sensor, but if a major electrical event such as a power surge affects the sensor, they can also act as privileged inflow channels from the sensor to the body.

BITalino is battery operated and uses a wireless interface for data transfer, guaranteeing that it’s completely independent from any high voltage power source during normal operation. To prevent electrical hazards, it’s important to make sure that BITalino isn’t coupled at any time with the mains or any other high voltage power source either directly or indirectly (via a third-party device or test equipment).

The power management block is fitted with a USB connector for charging (as shown in Figure 1)—as such, users should verify that the charging cord is disconnected prior to connecting BITalino sensors to their body. Failure to do so raises a major safety concern: increased risk of electric shock due to a potentially high common-mode voltage on the USB signals coupled with the low impedance path to the body via the electrodes. Furthermore,

TaBle 2Onboard peripheral specifications.

ECG EMG EDA ACC LUX LED

Principle Voltage differential

Voltage differential

resistance micro-Electro-mechanical System (mEmS)

Photo transistor –

Electrodes 2 or 3 3 2 – – –

bandwidth 0.5–40Hz 10–400 Hz 0–3 Hz 0–50 Hz – –

Input impedance 100GΩ @ 3pF 100GΩ @ 3pF – – – –

cmrr 110 db 110 db – – – –

range ±1.5 mV ±1.65 mV 0–1 mΩ ±3 G 360–970 nm –

Gain 1100 1000 2 – – –

consumption ~4 mAh ~4 mAh ~2 mAh ~350 uAh ~50 uAh ~10 mAh



when using testing equipment such as oscilloscopes or tapping directly into the UART pins for communication with third-party devices, users should make sure that these external devices have a medical grade or isolated power supply prior to connecting BITalino sensors to their body.

One more safety aspect that users should take into account is bias currents across the body and organs such as the heart. When connecting low impedance electrodes on the skin surface, the voltage and current levels don’t cause problems because skin has very high impedance. However, connecting electrodes to open wounds or body areas not covered by skin (for example, inside the mouth) can be dangerous because the skin’s high impedance isolation no longer exists.

What about Software?The BITalino toolkit has multiple software components, including the firmware on the MCU, the programming APIs, and the high-level application for the base station.

Although the BITalino MCU block uses an AVR-based chip similar to that of standard Arduino boards, the

Arduino bootloader and libraries introduce a considerable runtime overhead, leading to a high skew and jitter in the sampling rate. Along with the overall hardware design, BITalino has a purpose-built firmware, optimized to acquire a maximum of six analog and four digital channels at up to 1,000-Hz sampling with maximum performance, so it’s not yet compatible with the Arduino IDE.

Currently, BITalino can only be reprogrammed by using an in-system programmer (ISP), although users should read the original fuse settings and back up the firmware, if needed. All the sensors and peripherals on BITalino are compatible with popular do-it-yourself hardware platforms, meaning that people wanting to create their own biosignal-enabled embedded application can use an Arduino, Raspberry Pi, or other low-cost platform as the MCU block. Alternatively, users can also interface BITalino with their platform of choice via the UART.

We’ve attempted to provide maximum flexibility in any interaction with devices. Communication with BITalino can happen at the byte-stream level, using the

communication protocol implemented in the firmware. It’s also possible to choose from a growing number of programming APIs for languages such as Python, Java, and Matlab, among others, allowing users to communicate with BITalino and access sensor data in their own software applications.

At the user level, people can install our OpenSignals software framework (previously known as SignalBIT) and use it to visualize and record biosignal data in real time or to review previously recorded data. We designed this framework using a client-server and model-view-controller (MVC) approach, in which the back end is implemented in Python and the front end is a Web-based GUI. All the source code is available upon install, allowing users to customize the software to their preferences or to create purpose-built derived applications. Figure 3 shows the GUI for the main menu and real-time data acquisition screens, showing plots with real-world data for ECG, EDA, EMG (two muscle activations), and ACC (one full rotation over the z-axis).

Our ongoing work focuses on a software framework for mobile

(a) (b)

Figure 2. examples of physical connection between a BITalino board and the body for two of the onboard sensors: (a) electromyography (eMG) and (b) electrodermal activity (eDa).


devices—in particular, for the Android operating system (available upon request). Figure 4 shows the GUIs for two applications developed using this framework, which we call MobileBIT (see Figures 4a and 4f).

A major barrier to working with physiological sensors is signal processing. To tackle this issue, we’re working on a toolbox called BioSPPy (for BioSignal Processing in Python) that provides basic signal processing and feature extraction functions. Our goal is to continue growing the toolbox to include additional signal processing and interpretation methods.

Bitalinos in the WildWe’ve conducted several user studies and experimental activities to demonstrate that people with different backgrounds can use BITalino in their projects. Each experiment was application-specific and had the base BITalino board as a starting point. The most popular sensor by far has been the ECG. Given its use in medical and quality-of-life applications, this signal and some of its derived features are already widely known and familiar to most people.

In one case, we fit BITalino to a bicycle, using a conductive fabric on the handlebars for the electrodes. As Figure 4a shows, this setup enables heart rate monitoring just by holding the bicycle handlebars as you normally would, preventing the need to fit a chest strap or any other body-mounted device. To demonstrate the use of physiological signals to control third-party devices, we fit a BITalino freestyle on an arm band and a BITalino plugged to an electrically controlled door lock, letting the user unlock the door just by flexing his muscle (see Figure 4b).

One of the research lines actively developed within our group is related to the use of ECG signals for biometric applications via nonintrusive sensor technologies. One recent project adapted BITalino to a computer keyboard, enabling continuous monitoring of

ECG signals while the person used a computer (see Figure 4c). Another project used BITalino in a game station controller to assess the feasibility of ECG data acquisition in the context of game playing. A BITalino freestyle was fit inside the controller, with two electrodes placed on the outer shell, one in each grip handle; Figure 4d depicts our prototype, showing the right-hand-side electrode (the user’s hand covers the left-hand side).

A recent student project created a form factor that fit the BITalino on to the back of a mobile phone for heart rate monitoring.

The base board and sensors were broken down into individual components (freestyle variant); the student then merged BITalino’s digital blocks with the ECG board into a compact unit with two exposed electrodes, enabling heart rate monitoring whenever users place their fingers on the electrodes (see Figures 4e and 4f).

We envision the future for this area to be quite broad. Short - term progress will involve

(a)

(b)

Figure 3. OpenSignals software framework GUI: (a) main menu and (b) realtime data acquisition screen with eCG, eMG, eDa, and aCC.



the development of medical devices for low-income areas and developing countries: making these technologies

available at a low cost will give many people access to instruments that would otherwise be difficult to

achieve. Another interesting future di-rection will be in off-the-person sens-ing,14 with physiological data acquisi-tion devices integrated in the everyday objects with which the user interacts rather than via a body-mounted apparatus.

Around the toolkit itself, ongoing work in our group is focusing on adding Inter-Integrated Circuit (I2C) support for the firmware and APIs, ultimately letting BITalino interface with accessories over a digital bus. We’re also doing extensive work on accessories that can enable users to collect data from other physiological sources. In the long term, we’re focusing on making the MCU reprogrammable and on building up the suite of available software tools, with a special emphasis on signal processing and interpretation components.

Our goal with BITalino is that virtually anyone can have access to a basic set of tools, analogous to what the Lego Mindstorms kit does for robotics and the Arduino does for physical computing.

aCknOwleDGMenTSthis work was partially funded by Fundação para a ciência e tecnologia (Fct) under the grants Ptdc/EEI-SII/2312/2012 and SFrH/bd/65248/2009, whose support we gratefully acknowledge.

ReFeRenCeS 1. M. Banzi, Getting Started with Arduino,

Make Books, 2009.

2. L. Buechley and M. Eisenberg, “The Lily-Pad Arduino: Toward Wearable Engineer-ing for Everyone,” IEEE Pervasive Com-puting, vol. 7, no. 2, 2008, pp. 12–15.

3. D. O’Sullivan and T. Igoe, Physical Comput-ing: Sensing and Controlling the Physical World with Computers, Thomson, 2004.

4. E. Topol, The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care, Basic Books, 2012.

5. S.H. Fairclough, “Fundamentals of Physi-ological Computing,” Interacting with Computers, vol. 21, nos. 1 and 2, 2009, pp. 133–145.

(a) (b)

(c) (d)

(e) (f)

Figure 4. examples of several projects done with the BITalino platform—the most popular sensor has been the eCG: (a) bicycle handlebars with heart rate monitor; (b) a muscleactivated door lock; (c) a keyboard for continuous eCG acquisition; (d) a game controller fitted with an eCG sensor; (e) heart monitoring on a mobile phone; and (f) an android OS interface for heart monitoring.


6. H. Herr, “Exoskeletons and Orthoses: Classification, Design Challenges and Future Directions,” J. NeuroEngineering and Rehabilitation, vol. 6, no. 1, 2009, pp. 21–30.

7. L. Bos et al., eds., Handbook of Digi-tal Home care: Successes and Failures, Springer, 2011.

8. B. Graimann, B. Allison, and G. Pfurtscheller, eds., Brain Computer Inter-faces, Springer, 2011.

9. P. Petta, C. Pelachaud, and R. Cowie, eds., Emotion-Oriented Systems: The Humaine Handbook, Springer, 2011.

10. A. Helal, M. Mokhtari, and B. Abdul-razak, The Engineering Handbook of Smart Technology for Aging, Disability and Independence, Wiley-Interscience, 2008.

11. J.D. Bronzino, ed., The Biomedical Engineer-ing Handbook, 3rd ed., CRC Press, 2006.

12. T. Saponas et al., “Demonstrating the Feasibility of Using Forearm Electromy-ography for Muscle-Computer Interfaces,” Proc. SIGCHI Conf. Human Factors in Computing Systems, 2008, pp. 515–524.

13. A. Sano and R. Picard, “Stress Recogni-tion Using Wearable Sensors and Mobile Phones,” Proc. Assoc. Conf. Affective Computing and Intelligent Interaction (ACII), 2013, pp. 671–676.

14. H. Silva et al., “Off-the-Person Electro-cardiography,” Proc. Int’l Congress on Cardiovascular Technologies (CARDIO-TECHNIX), 2013, pp. 99–106.

15. W. Boucsein, Electrodermal Activity, 2nd ed., Springer, 2011.

16. G. Mathew, “Medical Devices Isolation: How Safe Is Safe Enough,” tech. report, Wipro Technologies, 2002.

the aUThORSHugo Silva is a Phd student at the Instituto Superior técnico (ISt), University of Lisbon (UL), Portugal. His work has been distinguished with several academic and technical awards, and his main interest areas include biosignal research and pattern recognition. Silva received an mSc in electrical and computer engineering from UtL. contact him at [email protected].

Ana Fred is a professor in the department of bioengineering at Instituto Superior técnico (ISt), University of Lisbon (UL), Portugal. Her research interests include pattern recognition, with application to data mining, learning systems, and behavioral biometrics. contact her at [email protected].

Raúl Martins is a professor in the department of bioengineering at Instituto Superior técnico (ISt), University of Lisbon (UL), Portugal. His research interests include biomedical instrumentation and sensors, electromagnetic imaging, physiological variables monitoring and modeling, and implantable devices. contact him at [email protected].

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