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Heterogeneous, Distributed On Body Computing in the WearIt@Work Project Paul Lukowicz1,2 , Oliver Amft1 , David Bannach2 , Gerhard Tr¨oster1 1 Wearable
Computing Lab ETH Zurich, Switzerland www.wearable.ethz.ch 2 Institute for Computer Systems and Networks UMIT Innsbruck, Austria csn.umit.at
Abstract— The paper deals with architectural aspects of the wearable system that will be used in the WearIt@Work project to bring wearable computing to real life industrial applications. It describes the vision of the project, specifies the architectural requirements resulting from this vision and describes the overall architectural concept. We also present first experimental devices and discuss a software framework designed to allow flexible distribution of applications through the system.
I. Introduction WearIT@Work is a European Union sponsored (IST FP6 program) research project aimed at bringing wearable computing into real life industrial applications. The vision behind the project is that wearable computing can do for mobile professionals away from the desk, what networked desktop computing has been successfully doing for the professionals in offices. Many work processes in industry and services involve tasks where a stationary IT infrastructure is of limited use only. Examples are the maintenance of industrial equipment in industrial plants, the work of medical professionals at the point of care, or many other jobs in the service industry. However, the mobile computing solutions of today are insufficiently effective because they force the user to focus his attention on the interaction with the system rather than on carrying out the actual task. WearIT@Work takes the next important step of innovation in addressing this shortcoming: It aims to develop a mobile computing platform that supports complicated tasks with a minimum of human-machine interaction and thereby enables mobile professionals to keep their attention focused on the interaction with the work environment. Moreover, the system should be truly wearable, integrating sensors, body area networks and processing units as unobtrusive as possible. This approach will broaden the scope of wearable applications to new areas such as the inspection of motorways, the maintenance of large technical plants, emergency services, as well as the distribution or consignment sale of merchandise.
A. Paper Contributions and Organization The implementation of the above vision requires research in many areas including work-flow management, human computer interaction, context recognition, and wearable system design. This paper deals with the latter. It presents a concept for a distributed, heterogeneous on body computing system that will be used as a basis for the project. We begin with an overview of the specific requirements that the project vision imposes on the system (section II). We then describe the general ideas behind our system and its architecture (section III). Section IV then provides some example of experimental devices implemented by our group as a first step towards the envisioned system. We finish with a description of a software framework in section V supporting distributed processing of context recognition tasks, that is currently under development. B. Related work Wearable computing systems currently available include PC-based and PDA-based systems relying on industrial standard components. To this group belong system on PC-104 boards, Laptops or Mini-PC computers [5, 18, 19] as well as PDA-based wearable systems, e.g. MIThril [12]. Wearable Systems based on custom design include low end special purpose appliances such as smart watches, badges [13, 14, 16], garments [15], medical monitors [4, 11] and multiple purpose wearable computers, providing more functionality and flexibility. To this group of wearable computers belong the SPOT [7], ITSY [8] as well as WearARM [2, 10].
Fig. 1. Classes of wearable computing systems
Systems relying on standard PC- or PDA-based solutions do not reach a high degree of wearability, whereas the low end custom designs are bound to their specific task. The custom multiple purpose wearable computers establish a compromise between high and low end in providing central processing performance and a wearable system design. Integrating these devices with application-specific sensors and an appropriate networking infrastructure yields a complete wearable system. Especially for contextual classification a wide variety of sensors can be used, requiring flexible interfaces at the wearable computer. Of the multiple purpose systems currently available, none address the ergonomic aspects, while providing sufficient connectivity options and computational performance, necessary for the envisioned wearable applications and context recognition work of the WearIT@Work project (see figure 1). II. System Requirements A. The WearIT@Work Vision The vision of industrial wearable computing put forward by the WearIT@Work can be summarized as follows: 1. Workers will wear information technology on their body (e.g. computers in the clothes, head mounted devices as a personal information display, small input and output devices for appropriate multi-modal human-computer interaction, voice for command control and wireless transmission of voice, video and data communication). The wearable system will be flexible enough to act as a platform for various services. 2. The mobile worker will be supported by a wearable computing system during the whole work process. The computer-support will be integrated into the working process, and the use of the system will be as casual as possible. 3. Context- and situation-aware applications will consider multiple criteria to decide which information has to be presented to the user in a particular situation. Sensors will detect the user’s context based on the state of the environment (e.g. state of machineries, state of production, relevant deliveries, etc.) via wireless connectivity. 4. Due to the permanent activity of the wearable computing system, it will be capable to continuously monitor the activities of a user in the real world. The state of the real world will be synchronized with its representation in the information system. The synchronization will not be the task of the user. Instead, the processes and the environment have to be designed in a way to allow for autonomous synchronization by the wearable computing system, or at least for synchronization with minimal effort of the user. 5. Use of on-line information and knowledge-sharing between the members of a team will enable real collaboration on-site. A collective working goal will remain collective even during the execution of the working tasks. 6. Knowledge will be shared between team members. Organizational knowledge will be enriched in this way. Spontaneous communication (voice or video) will enable a situation similar to a professional’s look over the shoulder of
an unskilled worker to support him during his training on the job. 7. The cognitive load on workers will be reduced. The personal information system will autonomously interact with the environment and the information systems of the team members. B. Consequences for the System Architecture For the wearable system implementing the above vision involves a number of challenges. On an abstract level these challenges can be summarized under four headings: (1) Functional requirements, (2) Information flow requirements, (3) Hardware Resource Constraints and (4) Physical Implementation constraints. Functional Requirements The key difference between the wearable system and a conventional mobile is the range of applications that the system needs to execute. Most of the time it is performing simple signal precessing and pattern recognition task devoted to tracking context and user activity. Periodically bursts of computing ranging from medium intensity classical mobile computing applications to high end vision processing and rendering are required. Information Flow To interact with the user and the environment the wearable computer needs sensors and inputoutput devices placed at certain locations on the body. Thus the display will be placed in the glasses on the head, the microphone on the chest near the mouth, the GPS antenna on the shoulders. The placement of the sensors depends on their function with motion sensors needed on the limbs, head, and torso, a camera preferably placed on the head, and environmental sensors (light temperature) best placed on the shoulders. This means that the system design needs to follow certain information flow patterns that imply a distributed system architecture. Hardware Resource Constraints The main resource constraint of a wearable computer is the available electrical power. The system is supposed to be operational during a whole day, mostly without access to an external power source. At the same time size and weight considerations limit available battery capacity. Physical Implementation Constraints To be accepted as an integral part of the users daily outfit a computer must virtually disappear from sight. Carrying a wearable computer must not in any way hinder the user’s activities or change his appearance in a socially unacceptable way. This leads to stringent form factor requirements. While overall system weight and size is an important factor there are additional, equally relevant issues that need to be considered. As described in ?? not all locations on the body are equally well suited for the placement of computer components. In addition shape is as important as size and we might. Flat flexible devices can be fairly large without being inconvenient. Another important issue concern communication infrastructures. In certain locations (e.g. on the torso) wires can easily be incorporate into clothing providing cheap communication channels. However in other locations, in particular between body parts and/or different pieces of clothing wires are irritating and diffi-
cult to conceal making wireless communication desirable, despite much higher power consumption. III. System Concept To comply with the above requirements the following design decisions have been for the WearIt@Work computing system. Simple Yet Smart Sensor Nodes. Since sensor nodes need to be distributed over the user’s body and outfit their size is particularly critical. So is the size and with it the capacity of the battery. In fact for many application autonomous sensor nodes are needed that will be able to function using nothing more then the energy scavenged from the environment. As a consequence optimizing power consumption is particularly important. In this context communication computation tradeoffs play a key role. It has been shown that for a wireless connection energy wise it is cheaper to do 100 computation steps then to transmit one bit. This means that whenever the data rate coming from a wireless sensor can be reduced through feature extraction computation this should be done. For our system this means that the sensor nodes are equipped with elementary processing power. In most cases it comes from an ultra low power RISC signal processor such as the TI MSP 430 or a special purpose VLSI circuit. Hierarchical Heterogeneous Sensor Networks. The design of the on body network connecting the sensor nodes and the central wearable computing unit is a tradeoff between power consumption (which is particularly problematic for wireless sensors) and the level of integration into the outfit (which is particularly problematic for wired interconnections). In addition issues of electrosmog (both subjective and objective) need to be taken into account. Furthermore the system management benefits of standard solutions such BlueTooth need to be weighted against the much lower power consumption of custom ultra low power RFtransceivers. The above considerations have lead us to a heterogeneous network concept that follows the hierarchy implied by the body physiology and the partitioning of the outfit into different pieces of clothing. In general we aim to connect sensors that can be placed on a single piece of clothing using textile interconnections. This gives us low power consumption and excellent integration. In terms of topology serial buses such as I2C or RS 485 are the preferred choice. To further reduce the complexity sub-buses might assigned to sensors placed on a particular body part. Since running wires between different pieces of clothing is undesirable wireless transmission will be used for this purpose. Ideally there would be a single wireless transmitter to which all the subnetworks on a single piece of clothing would be connected. In some cases sensors that must be take on and off quickly and placed at different location at different times would also use wireless transmission. A wireless interface will also allow the system to interface sensors integrated in the infrastructure. In terms of protocol a mixture between BlueTooth and custom low power systems will be used. Lightweight Wearable Client The combination of hardware
resource and physical implementation constraints with th fact that the system must have a battery life of at least one working day implies that it is not feasible to use a high end CPU. Thus the wearable system will not be able to perform particularly computation heavy tasks such as 3D rendering, some augmented reality operations and certain image or audio processing. The concept pursued in the project is to outsource such tasks to either an external server or a special purpose device and keep the central processing unit a small and low power lightweight client. Under this concept the central processing unit is in charge of context monitoring and recognition, the user interface, local data management, and performing simpler task such as retrieving manual pages, annotating recorded video etc. Certain often needed, computation intensive signal processing tasks will be implemented as autonomous special purpose devices connected to the main system through an appropriate interfaces. Examples of such devices would be a dedicated speech recognition box, or a dedicated special purpose circuit for certain computer often occurring vision tasks such as hand or marker tracking. For many such tasks it has been demonstrated that such special purpose implementations can consume up to several orders of magnitude less power then a general purpose CPU. Obviously for many functions the dedicated special purpose hardware solution is not feasible. this is true for operations that either do not occur often enough to justify a special hardware development or where a dedicated implementation does not offer a significant benefit. In such cases we aim to use an external server to execute the computation with the wearable being merely a data collection and result display terminal. The main constraint in such a case is bandwidth required to transfer data and results between the wearable and the server. As a consequence appropriate compression schemes and computation partitioning concepts will have to be found for each application. The above decision are based on more generic work dealing with methods for the design of distributed wearable systems described in [1]. IV. Experimental Hardware Overview Working towards a vision of an industrially viable system based on the principles described above we have implemented a number of experimental devices that are currently being evaluated and used initial wearable application studies. They include the QBIC wearable computer, a variety of smart sensor nodes and first textile interconnection systems. A. The QBIC Central Computing Unit Building on the experience with our previous wearable system, the WearARM [10], the Q-Belt-IntegratedComputer (QBIC) has been designed at our Lab1 with an emphasis on ergonomics and wearability while at the same time providing most possible functionality and flexibility. To this end the design process has been oriented around 1 Wearable Computing www.wearable.ethz.ch
Lab.,
ETH
Zurich,
Switzerland,
human design issues which have provided the boundary conditions for the system architecture and electronic implementation choices. The main design decision can be summarized as follows: 1. The core part of the system is integrated in a belt buckle. In addition to housing the computer the buckle is also fully functional in terms of closing and holding a belt. 2. Batteries and connectors are integrated in the belt, which can also be used to attach additional peripherals. 3. The system emphasizes wireless interfaces, which are integrated on board. The main mode for connecting nonwireless peripherals is USB which provides access to a wide range of devices with compact connectors. In particular it has been decided not to include an compact flash interface which would require too cumbersome connectors. 4. The system contains enough FLASH and SDRAM memory to be able to run key elements of the Linux operating system without any external mass storage. 5. The QBIC system is physically divided into two parts: the main board and the extension board (see figure 2). The main board contains all essential components of the system and can be used stand-alone. Additional memory and periphery devices are located on the extension board, which is attached to the main board through a flexible printed circuit board (PCB) substrate. A.1 Mainboard Architecture A diagram of the QBIC system architecture is shown in figure 2. The following sections summarize the detailed system architecture and interface options.
which provides a set of integrated DSP instructions. Additionally the PXA263 offers 32 MBytes of internal FLASH memory for non-volatile storage of operating system, applications and user data. A total capacity of 256 MBytes SDRAM is soldered as processor memory on the main board. This relatively large capacity for a mobile devices allows a wide range of future applications, also in the field of multimedia (audio and video processing). The Intel PXA260-family has three integrated serial interfaces (UARTs): a full-functional, a Bluetooth and an Infrared-UART. The full-functional UART can be used as fully EIA/TIA RS-232 compatible serial port with up to 460 kbps. This interface is intended for classic wired sensor attachment, providing simple point-to-point operation. It is also used as I/O-console for the operating system shell. The Bluetooth-UART is directly connected to the Bluetooth-Module on the extension board and handles up 921.3 kbps. The third UART can be connected to a low power RF transceiver (TR1001 from RFM). This kind of wireless communication consumes much less power than wireless LAN (WLAN) or even Bluetooth. Data rates of up to 115.2 kbps in Amplitude Shift Keying mode can be reached, sufficient for input devices with a relatively low data rate, like PS/2. Another important peripheral interface on the mainboard is the VGA DAC. It converts the 16 digital signal lines from the PXA263 built-in LCD controller to an analog standard VGA-signal with a resolution of 640 x 480 pixels and 16bit colour depth. VGA is still the standard display interface, sufficient for Head Mounted Displays (HMD), or similar visualisation devices, worn in front of the users eye(s).
Fig. 3. The QBIC main board
A.2 Extension Board Architecture
Fig. 2. The QBIC system architecture
The core of the system is an Intel XScale family processor: the PXA263. Main features of this processor include scalable clock frequency up to 400 MHz, dynamic corevoltage scheduling as well as several power-save modes. The PXA26x family utilizes the ARM v5TE architecture,
As the extension board forms an optional part, different interfaces, combined with a dedicated belt could be selected for certain applications. The current version contains peripheral components that are essential for a majority of intended applications. In order to save power all interface chips can be switched off or set to idle mode by software, reducing power dissipation. In the current version a slot for a MiniSD storage card is included to extend the built-in FLASH memory of the
XScale processor. This memory is predestined to store user and sensor data or additional application software. The USB host controller function is provided by the extension board. The controller provides two independent ports and can handle USB low-speed and full-speed devices (1.5 and 12 Mbit/s). For diversified wireless connections a Bluetooth module was integrated on the extension board. The used module is a class 2 device (transmit power typical 1.5 dBm for a range up to 10 meters). A.3 Electronics Implementation A flexible substrate connects the extension board to the main board and is directly integrated into the substrate of the extension board which provides a high reliability connection. Since the system dimensions should be kept to the viable minimum, all components were selected in their smallest available form factor, using ball grid packages where possible. As a result of a dense placement we achieved a PCB for the main board which is 44 by 55 millimeters in dimension. The extension board is even smaller and measures 48 x 31 millimeters (not including the flex connection). A.4 Integration into the Outfit Wearable computers should under no circumstances diminish our comfort. The actual comfort of wearable computers depends on a variety of factors. A selection of these factors have been suggested by F. Gemperle et al. [6]. As a way to satisfy those requirements in our system, we have decided to integrate the QBIC seamlessly into a belt (see figure 4). This means, that its functionality is not limited to that of a computer but it also incorporates the functionality of a belt, i.e. to support a pair of trousers and to embrace an aesthetic appearance. As an accessory, a belt is not subject to typical cleaning procedures of normal clothes. Additionally it can be worn for both functional and aesthetic reasons. Since various people prefer different styles we decided to create a variety of belts (different color, texture or material) with a similar buckle that incorporates the main electronic components. The buckle can also be taken off so the user can transfer the computer onto different belt types. When the user decides not to wear a belt at all (e.g. whilst wearing an evening dress or shorts) he/she can use the computer stand-alone. The belt of the QBIC consists of two layers of leather with a data cable in between. The data cable contains all possible power and data lines, whether used or not. The connectors can be assembled according to the application specification onto a connector-block, which is pressed onto the data line in three possible locations. In this way, the belt operates as extension bus of the computing system. Our first version of the QBIC contains a flat battery integrated in the layers of the belt, a connector for an additional external battery, two connector blocks which incorporate USB and RS-232 connectors and some extra buttons together with a connector block containing a VGA connector.
B. Sensor Network B.1 The PAD’Net System PAD’Net [9] has been implemented as a multistage sensor network with a hierarchy that reflects the anatomy of the human body. The hierarchy allows information from different, logically related sensors to be fused on their way to a central node limiting the amount of required cables and transmitted data. The current version of the system is based on wired transmission aiming at using textile interconnections in the user’s garment for data transmission. In addition, different wireless versions have been demonstrated including one based on Bluetooth technology (see below). In wireless networks, the aggregation of sensor readings becomes even more important. All nodes within the network are based on a common module depicted in figure 7. The module incorporates a MSP430149 low power 16-Bit mixed signal microprocessor from Texas Instruments running at 6 MHz maximum clock speed. The current common module version reads out up to three analog sensor signals including amplification and filtering and handles the communication between modules through dedicated I/O pins. In this way the common module can be a host for different sensor types, e.g. accelerometers, gyroscopes and magnetic field sensors that can be mounted on the module. Figure 7 shows the common module in configuration with a 3D-magnetic field and a 3D-accelerometer sensor unit. B.2 The Soundbutton System Our long term vision for the hardware implementation is that of a fully autonomous device containing all specialpurpose custom processing circuits, wireless communication interface and its own power supply in a high density button like package. The energy required for such autonomous systems, should be harvested from the environment [3]. The prototype described in this paper represents an intermediate step towards such a system [17]. It uses a MEMS miniature microphone, analog and digital preprocessing and transmits selected features. However it relies on off the shelf components rather then custom designed circuits for processing, communication and power supply and is implemented on a conventional credit card size PCB. A block diagram and a picture of the hardware can be seen in Fig. 8. The hardware contains a MEMS microphone from Knowles Acoustics (SP0103NC3-3), a MSP430F129 microcontroller and a DR3001 wireless transceiver from RFM working at 868 MHz with a data rate of 115.2kbps. The output from the microphone (internal gain of 20) is amplified by 2 and low-pass filtered before being ADconverted at 4.8kHz and 8bit with the internal ADconverter of the microcontroller. The output power at the wireless transmitter is −1.25dBm, which is sufficient to cover short distances from the wrist to the torso or hip. The hardware is powered by a Fuji NP-40 battery with 3.7V, 710mAh which has a lifetime of nearly 2 weeks.
MSP430
UART0
AD−Converter
Mic
LP
RS232
UART1
RFM Transceiver
Fig. 4. The QBIC system in the buckle of a belt Fig. 8. Prototype
Fig. 5. Left: The QBIC computer (buckle) is detachable from the belt and can be used stand-alone (right) Fig. 9. The Bluetooth Sensor Platform with textile envelope
B.3 Bluetooth Sensor Interfaces
Fig. 6. QBIC Buckle architecture
To be able to easily use BlueTooth for a variety of sensor nodes a generic interface has been built. It contains a Bluetooth module compliant with the RFCOMM standard, a serial interface and a variety of power supplies. Figure 9 shows the module integrated into a textile envelope. The modules is able to provide regulated 3.3 or 6 Volt power to any sensor node, and function as a serial pipe transporting the serial output of the sensor module through the BlueTooth RFCOMM protocol. The serial port can deal with the standard RS 232 signal level as well as with 3.3V interfaces. B.4 Textile Integration Existing conductive fabrics that are used for EMI shielding or static dissipation contain metal or carbon fibers and provide electrical conductivity on the whole surface. Data transmission requires separate conductor lines in the fabric. Figure 10 shows a woven fabric that has been developed for our experiments. This material contains insulated metal fibers and provides a tight mesh of individually addressable wires that can be used for textile networks (long distance transmission lines) and also for textile circuits (short distance conductor paths):
Fig. 7. The PAD’Net Platform, left: architecture, right: sensor node with an acceleration sensor.
B.4.a T-BoNe (Textile Body Network). T-BoNe is an example of a textile network infrastructure for the connection of electronic modules that are distributed all over the clothing. The electronic components either are attached to the fabric with fixed connections or use wireless technologies for communication with the textile network. Figure 10 right shows a prototype of T-BoNe with several movement sensors that are connected utilizing textile transmission lines. This is a first approach to build Motion Aware Clothing (MAC).
Fig. 10. Conductive Fabric (left) and an example of its application in Motion Aware Clothing (right)
V. Software Framework A. General concept Developing systems for on-line context recognition using the concepts described above, implies building a streaming network for the sensor data. The sensor readings have to be continuously acquired from one or more sensors under real-time conditions and then handed into diverse filters for conditioning, pre-processing and feature extraction. The filtered data stream is then fed into classifiers for the contextual recognition. The classifiers itself are often a nested structure or network of well known classification algorithms, e.g. Hidden Markov Models or Bayesian classifiers. Most of the pre-processing and feature extraction tasks are common in every recognition system and there is little sense in re-implementing them for each system. This framework aims at a general solution for the streaming network behind on-line contextual recognition systems. Our goal is to create an infrastructure that facilitates the configuration of such systems using standard pre-built and customized components with clearly defined interfaces. Some components are too complex or computational expensive to run on a constrained autonomous sensor or even on a light-weight multiple purpose wearable computer. Also, to keep a low power dissipation for the wearable system, off-loading computation to a remote processing system is considered valuable. Accounting for this, it is necessary to keep this framework as flexible as possible and provide means for remote processing. Moreover, to allow sharing of information between hierarchical components or in cooperating systems, information interfaces are required. B. Detailed implementation The components are composed of tasks for acquisition, pre-processing (feature extraction), and classification. Tasks may be configured to run on different hosts to cope with limitations of wearable computing environments stated above. In implementing the framework, we have chosen to connect the tasks through TCP sockets, building a network for processing real-time sensor data. Implementations of often used standard tasks are provided as C++ objects in a toolbox. The toolbox can summarize tasks that run on the same host. Each task object in this toolbox has a number of in-ports and out-ports relating to its functionality. An out-port of a task object can
Fig. 11. Example Configuration of a Software Framework
be connected to an in-port of another task object, establishing a direct connection. This way the overhead of TCP sockets is avoided, wherever possible. If necessary, dedicated client/server objects can provide TCP connections to tasks running outside the toolbox. Figure 11 shows two configured toolboxes running on a wearable computer and a remote hosts respectively. The small boxes represent the tasks with in-ports on the top side and out-ports on the bottom side. Toolbox2 runs on a fast machine and does the main part of the data processing. It receives two TCP streams from toolbox1 which is running on a small wearable device. Toolbox2 logs its classification results and offers it on a TCP port. This way the results can be received by an application on the wearable device or elsewhere. In the phase of classifier development the framework allows to sequentially migrate tasks from the remote host to the wearable computer as deployment advances and algorithms stabilize. VI. Conclusion VII. Acknowledgments The multi-disciplinary work presented in this paper involves several people at our Lab. The authors would like to thank Michael Lauffer, Stijn Ossevoort, Holger Junker, Mathias St¨ager and T¨ unde Kirstein all with the Wearable Computing Lab., ETH Zurich for their contributions. We also thank Michael Boronowski, Center for Computing Technologies c/o University of Bremen, being the WearIT@Work project ’father’, for his enthusiasm in establishing and realization of the the project.
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