*3.1.2 System description*

The wearable data capture unit (WDCU) acquires data from eight channels of EMG through an arm gear and eight channels of EEG data through a head gear and transmits the data simultaneously to the PC using a USB cable (**Figure 4**). The design of this arm gear has been previously reported in a separate paper by the authors along with design and testing of the amplification circuit [37]. The software running on the PC processes these signals from 16 channels and combines them in a time locked manner for presentation on the screen as real-time feedback showing muscle over-activation and under-activation as cartoon characters (EMG signal as agonist-antagonist koala bears climbing up or down a tree, EEG signal as a smiley face). While EMG signals are used as feedback by squaring and averaging the amplitude within a running window updated every 10 milliseconds, the EEG signals were converted to frequency band using a Fast Fourier Transform and the alpha

**Figure 4.** *SynPhNe learning model platform and user-interface.*

band power (8–13 Hz) was used to represent a relaxed state, updated every 10 s as a proportion to total power in the 1–35 Hz frequency band. While EMG was sampled at 1000 samples/sec, EEG was sampled at 256 samples/s.

The goal of both the feedforward and feedback is to successfully attempt a movement or physical task while maintaining a relaxed brain-muscle state pre- and post-action comparable to resting state. If effort results in a deviation from resting state, return to resting state post-effort should be immediate. Brain and muscle influence each other too. Losing attention partially or fully may result in loss of ability to imitate the feedforward video and respond to feedback. Incremental changes in self-regulation are presented visually in the real-time user interface, which then provides an impetus for the patient to self-regulate further.

**Figure 4** depicts the user interface on the computer screen. The subject observes the video as the feedforward in order to imitate it with the same speed. The koala bears, and tree serve as agonist and antagonist muscle EMG feedback during such imitation. The subject attempts to activate the appropriate muscle to raise the brown bear (agonist) to the top of the tree while keeping the gray bear (antagonist) as steady and close to the bottom of the tree as possible. The yellow smiley face represents EEG frequency band feedback as a measure of a relaxed brain state which needs to be maintained as best as possible while imitating the video-based physical movement or task.

In both the clinical studies, the subject tried to imitate an exercise and task practice video sequence running on the computer screen, while attempting to correct maladaptive over-activation and under-activation in opposing muscle pairs displayed on the same screen. Using a slower speed of execution than normal allowed proximal joints of the upper limb to stabilize and reduce temporal demands on the subject [38]. The slow-paced video sequences allowed time to train relaxation between repetitions. Also, the need to achieve a relaxation goal immediately after activation encouraged the subjects not to over-activate the muscles and to moderate their effort. This strategy was found to delay the onset of high dynamic muscle tone and allow for better repetition-based performance based on greater number of successful relaxations. When subjects experienced difficulties in being able to relax their muscles, they intuitively made postural corrections to let go and relax deeper before the next muscle activation. EMG thresholds displayed on the software gave them a clear indication on activation and relaxation targets appropriate for training, which were based on previously calibrated maximum voluntary contraction (targets were up to 40% of maximum) and resting state EMG respectively, for various muscle groups. In this paper, analysis of only the EMG peaks data as seen during activity and immediately post activity repetition is highlighted. The EEG and other

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**Table 1.**

*Demographic of recruited subjects.*

*Restoring Independent Living after Disability Using a Wearable Device: A Synergistic…*

metrics will be reported separately in subsequent papers since the primary objective of this paper is to highlight the thinking behind the user-interface design and the

In Trial 1, 15 adult chronic stroke subjects with a hemiplegic hand (31–69 years;

RH001 57 Female 22 Hemorrhage RH002 44 Male 21 Infarct RH003 54 Male 12 Infarct RH004 61 Male 25 Infarct RH005 69 Male 21 Infarct RH006 38 Male 18 Hemorrhage RH007 48 Male 18 Hemorrhage LH001 31 Female 32 Infarct LH002 53 Female 18 Hemorrhage LH003 59 Female 37 Infarct LH004 62 Male 8 Infarct LH005 57 Male 10 Hemorrhage LH007 65 Male 45 Hemorrhage LH008 62 Male 15 Hemorrhage

**poststroke**

**Nature of stroke**

4 females, 11 males) were recruited for the study (**Table 1**). In Trial 2, 10 adult

**Subject code Age Gender Months** 

Mean 54.3 21.6 Std. dev. 10.3 10.0

Mean 60.0 32.3 Std. dev 7.6 20.7

NRH001 69 Male 21 Infarct NRH002 60 Male 28 Infarct NRH003 57 Female 23 Hemorrhage NRH004 59 Male 7 Infarct NRH005 65 Male 7 Infarct NRH006 46 Male 60 Hemorrhage NRH007 67 Male 49 Infarct NLH001 61 Male 21 Infarct NLH002 45 Male 49 Infarct NLH003 62 Male 69 Infarct

*DOI: http://dx.doi.org/10.5772/intechopen.86011*

pre and post clinical outcomes.

Demographics of subjects in Trial 1

Demographic details of subjects in Trial 2

**3.2 Study methodology**

*Restoring Independent Living after Disability Using a Wearable Device: A Synergistic… DOI: http://dx.doi.org/10.5772/intechopen.86011*

metrics will be reported separately in subsequent papers since the primary objective of this paper is to highlight the thinking behind the user-interface design and the pre and post clinical outcomes.
