El pescado, fuente de ácidos grasos de la serie n-3

Accurate assessment of motor abilities is important in selecting the best therapies for stroke survivors. Sensor glove-based systems represent one of the most important efforts aimed at acquiring hand movement data. The process of tracking a hand generally involves calculating some of the properties of the hand (i.e. position, orientation and pose). There are several documented methods for position tracking when using glove- based input [351], the most common being; optical tracking [75, 115, 337, 389], magnetic and inertial sensing [87, 103, 189, 190, 293], mechanical sensing [46, 280, 358, 369] and acoustic tracking [274, 275]. In practise, a combination of these methods are often used for hand/finger tracking as each solution has its own advantages and disadvantages.

3.2.1.1 Mechanical Sensing

Conceptually, mechanical sensing is perhaps the simplest approach to position tracking and typically involves using some form of direct physical linkage between the target and its environment. For finger tracking this typically involves using electromechanical transducers such as potentiometers or shaft encoders, positioned at the fulcrum point between two articulated segments of the finger (i.e. adjacent phalanges of the finger). As the finger bends, the articulated segments change shape and the transducers move accordingly. The subsequent electrical output of the transducer can then be sampled and used to estimate the joint position of each segment of the finger.

Finger tracking of this kind is typically quite robust and cheap to design. However, it is difficult to develop a sensor glove based on these principles which is adjustable for multiple users with various sized hands, as the measurement is derived from mechanical components which need to be carefully positioned at the intersection of each joint. Adjustable devices have been developed of this kind, however the added mechanisms required to achieve this often clutter the device, making it cumbersome and difficult to use. Furthermore, such devices are often aesthetically disconcerting, which can have a negative impact psychologically on patient compliance with therapy.

3.2.1.2 Optical Tracking

Optical tracking typically refers to one of two methods used for real-time spatial track- ing of an object, either by tracking visual markers, typically coloured patterns, see Figure 3.13, or alternatively using the silhouette method, see Figure 3.24, which detects objects by their outline using an edge detection approach.

Marker based optical tracking relies on the ability to triangulate the three dimen- sional position of a marker in real-time with respect to a camera’s frame of reference. This type of tracking system is robust and useful when the interaction space of an object is known or fixed and when there is sufficient computation power available to process large numbers of images quickly. To be useful for real-time hand tracking, an optical tracking solution needs to be able to process camera frames and track the hand, at a minimum of 30 frames per second.

Figure 3.1: Example of a colour glove used for optical marker based tracking.

A great deal of work has also been done on natural gesture tracking, i.e. tracking the naked hand without a glove. This is typically achieved by detecting the silhouette of a hand through edge detection [206] or template matching [346]. While this form of hand tracking allows for the most natural user experience, tracking the hand without any invariant tracking features is a complicated task. Camera systems are extremely sensitive to environmental changes such as variation in natural light, the casting of shadows by other objects, the reflection of light off surfaces, all which can have a dramatic affect on the observed color of objects in view. The observed shape (silhouette) of the hand also changes dramatically as its ordination is changed, making it difficult to identify. Hence, a robust model is required to track the hand as it moves around the work space.

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Image:Color glove, source: MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), [389]

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Figure 3.2: Example of silhouette method used for hand detection.

The major drawback of camera based systems is that they are restricted to the space in which the cameras are placed. Additionally, optical tracking systems suffer from visual occlusion of the fingers or markers, that is the obscuring of an object as it moves behind another object, due to the cameras fixed reference position [99, 190]. To solve this problem, additional cameras, each with a unique perspective of the workstation can be used [147, 376]. However, adding additional cameras increases the amount of processing required to detect objects and given constrains, might not be a feasible solution.

3.2.1.3 Inertial & Magnetic Sensing

An alternative sensing method used in sensor gloves is based on local magnetic actua- tion. The availability of Micro Electrical Mechanical Systems (MEMS) technology has resulted in the development of tiny, low-cost Inertial and Magnetic Measurement Sys- tems (IMMS) devices that can be easily attached to textile clothing without impairing or restricting movement. An IMMS is an electronic device that consists of a combination of different inertial sensors which measures orientation and gravitational forces. An IMMS typically consists of a combination of accelerometers, which measure changes in accel- eration, gyroscopes, which detect changes in rotational attributes including pitch, roll and yaw and magnetometers, which assist calibration against orientation drift. IMMS are ideal for measuring motion in compact environments where space and weight are of concern, they are self-contained, they do not require line-of-sight for measurement and are not sensitive to interference from electromagnetic fields or ambient noise. They also have extremely low latency (typically a couple of µs) and can be measured at relatively high rates (thousands of samples per second). For this reason they are an excellent means for measuring the position and rotation offset of the hand.

However, to measure finger movement using this technique would require three sep- arate IMMS for each finger and another 2 for the thumb, totalling 14. Suitable devices for this task, that is IMMS which are small and light enough are expensive. Designing a nimble sensor glove suitable for use with a patient suffering from movement weakness, housing 14 IMMSs, not to mention the additional peripherals and supporting electronics required for their function, as well as a micro-controller, power supply and communica- tion device, would be a difficult task. In addition, although each IMMS can be sampled at relatively high rates, the computational speed required to sample 14 IMMS at a suffi- cient sample rate to attain fluid motion capture is high. Another disadvantage of IMMS for tracking is that they typically suffer from accumulated error. This is because the current estimation of position is based on previous estimation of position, therefore, any errors in measurement regardless of how small are accumulated. This leads to a drift in position measurement (i.e. an increasing difference between where the system thinks it is located and its actual location) [266].

3.2.1.4 Acoustic Tracking

Acoustic sensors use the transmission and sensing of high frequency audio signals to track motion. Acoustic ranging systems typically operate by timing the flight duration of a brief ultrasonic pulse. In practise, this is accomplished through a series of transmitters and receivers, where the transmitters (speakers) are usually positioned on the object to be tracked, in this case the glove itself, while the receivers (microphones) are positioned around the tracking environment, in this case a display device such as a TV or monitor. The transmitters take turns transmitting a short radio burst, which are subsequently detected at different times by the receivers, depending on their distance away from the signal source. Then using a triangulation algorithm, the relative position of the glove with respect to the display device can be determined.

Acoustic tracking may be appealing in situations were both optical and inertia/- magnet sensing are not practical. For example, magnetic tracking is easily disturbed by metal objects, which might be common in the application domain. Likewise, optical tracking is sensitive to background lighting and suffers from occlusion [80]. While this technique has be used to estimate the location and orientation of the hand, and in the- ory could also be used to estimate the position of each of the segments of the finger, in practise the latter is not feasible due to physical properties of ultrasound.

In general, acoustic tracking faces a number of problems for hand tracking. Acoustic sensing is sensitive to environmental dynamics as it is subject to reflection and occlusions

[335]. Acoustic sensing also suffers from high latency due to fly time delay and as a results can only be sampled at a limited rate [274].