Table 1 Summary of research on e-textile development for neurological rehabilitation

Tormene, 2012, [32]

Prototype design and validation

Trunk motion data from e-textile garment.

CE

1 healthy subject

Trunk movts

CE and intertial sensor readings.

Same accuracy as inertial sensor in sagittal plane.

Mattmann, et al., 2007, [41]

Feasibility/Pilot study

E- textile shirt to classify body postures

CE

8 Healthy males

1. Sensing shirt worn during 27 postures

2. Worn during trunk rotation exercise

1. E- textile sensor data and observation.

2. E- textile sensor data.

1. 25/27 postures classified with 97 % accuracy after 6 reps. 80 % accuracy when 3 reps and 65 % for a new user.

2. Can distinguish 4 grades of speed and reps.

Lorussi et al. 2004, [36]

Prototype design

E- textile sensor to monitor arm position

CE

Not reported

Subject wearing sensing sleeve pointing at targets

Comparison between calculated position of arm and true position

Relative error between true and calculated position 4-8 %

Tognetti, 2005, [30]

Prototype design and validation.

Sensing shirt to measure UL movement.

CE

Not reported

1. Measuring UL posture.

2. Measuring UL movement.

1. Avatar posture, expert opinion.

2. CE, electrogoniometer readings.

1. 100 % accuracy.

2. Divergence at some angles. Some loss of synchronisation.

Giorgino, Tormene, Lorussi, et al. 2009, [37]

Intersubject and inter- exercise variability.

Wearing an e-textile shirt.

1. Intersubject variability.

2. Interexercise variability.

CE

1. 3 healthy subjects

2. 1 healthy subject

1. Shoulder flexion.

2. Three UL exercises.

1. CE sensor readings

2. CE sensor readings.

1. There was low intersubject variability.

2. Each exercise showed clear variability in the pattern of results.

Giorgino, Tormene, Maggione, et al., 2009, [38]

1. Sensitivity and specificity testing

2. Pilot rehab study

1. Sensitivity and specificity of a sensorised shirt.

2. Acceptability of sensorised shirt.

CE

1. 1 healthy subject

2. 13 sub acute stroke patients

1. UL exercises performed.

2. Rehab device used on ward.

1. CE sensor readings, expert opinion.

2. 10 Qualitative questions.

1. Three shirts had adequate sensitivity & specificity. Refined sensor position had better results.

2. Good acceptability for users

Giorgino, Tormene, Maggioni, Pistarini, et al., 2009, [39]

Sensitivity and specificity testing

Evaluate sensitivity and specificity of a sensorised shirt.

CE

1 healthy subject

7 UL exercises.

CE sensor readings, expert opinion.

Exercises that stretch a fabric can be reliably classified.

Giorgino et al., 2007, [25]

Prototype design

1. Develop e- textile system that classifies exercises for neuro rehab.

2. Between session variability of the sensorised shirt.

CE

1. 1 healthy subject

2. 2 healthy subjects

1. 11 UL rehab exercises.

2. 11 UL rehab tasks. Shirt doffed; donned after 1 h. Exercises repeated.

CE sensor readings.

1. Redesign resulted in greater differences between readings.

2. 7 of 11 exercises were classified incorrectly when shirt was reapplied.

Lorussi et al., 2005, [26]

Prototype design and validation

1. Develop sensing glove that recognizes hand positions.

2. Recognize novel hand posture.

CE

20 healthy adults

1. Calibrated glove 32 hand postures repeated randomly.

2. Novel posture of hand held.

1. CE sensor

2. CE sensor, not stated.

1. 100 % recognition. 98 % recognition if removed and worn again.

2. Average error measuring joint angle 4 %.

Cabonaro et al 2014, [24]

Prototype design and validation

Compare e-textile motion sensor glove with optical tracking.

KPF sensors

5 healthy subjects

Repeated natural hand movts.

KPF sensor readings, optical tracking system.

Accuracy of glove slightly less than commercial electrogoniometer.

Preece et al., 2011, [27]

Prototype design and validation

1. Investigate output of KPF sensor in a sock, during walking.

2. Feasibility of predicting gait events using sock with KPF sensor.

KPF sensor

20 healthy adults

Walking wearing instrumented sock; shod and unshod.

KPF strain sensor, 3D video gait analysis.

1. Graphed sensor values and kinematic signals show similar characteristics.

2. Accurate HL & TO predicted offline HS prediction less accurate.

Sung et al. 2009, [29]

Prototype design and validation

Identify human movement during walking and running using e-textile sensors.

Knitted stainless- steel yarn sensor

5 healthy male adults.

Walking and running wearing e- textile suit.

e-textile sensor readings.

Similar results running & walking. Increased speed; individual habits insignificant.

Yang et al., 2010, [33]

Prototype design and validation.

Develop e-textile sensor system to monitor movts and posture.

20 Knitted sensors

Not specified.

Fast walking, slow walking & falling down.

E-textile sensor readings.

Sensor signal patterns differed for each condition.

Shu, et al., 2010, [35]

Prototype design and validation

Design e- textile sensor to monitor plantar pressure during gait

Knitted conductive sensor coated in silicon

8 healthy males

Subject wearing sensing innersole stepping and standing

Sensor CoP during standing, one leg stand, heel strike and push off compared to CoP on force plate.

CoP relative difference

Standing 7.9 %, One leg stand 9.9 %, Heel strike 0.5 %, Push off 2.2 %.

Tognetti et al. 2014, [31]

Prototype design and validation.

Compare KPF goniometers with electrogoniometers and inertial measurement units.

KPF sensors.

Not specified

KPF sensor over knee joint. One legged sit to stand at varied speeds.

KPF sensor, inertial measurement unit, electrogoniometer.

The KPF goniometer followed dynamic knee movts (maximum error 5°).

Shyr et al., 2014, [28]

Prototype design and validation

Measure the flexion angle of elbow and knee movts.

Elastic conductive webbing

1 healthy adult

Repetitive elbow and knee flexion/extension.

Protractor, e-textile sensor

Good relationship between e-textile sensor and joint angle.

Munro, et al., 2008, [40]

Reliability and validity

E- textile sensor to control audible biofeedback of movement pattern.

CE

5 female and 7 male athletes

Intelligent knee sleeve worn during hopping and stepping activities

Kinematic data, and audible feedback signal compared knee angle (goniometer)

Able to reliably distinguish between shallow and deep knee flexion.

Helmer et al., 2011, [42]

Pilot study

E- textile sensor to 1. measure knee movement and

2. Trigger auditory biofeedback to change kick pattern

Not specified

Not specified

E- textile sensorised leggings worn during kicking.

E- textile sensor data compared to 3D video analysis

1. Reliably measured max knee flexion during kicking < 10 % error

2. E- textile triggered audio signal. Change in kicking pattern post biofeedback training.

Farina et al., 2010, [16]

Prototype design and validation

Design electrode grids for recording EMG.

Stainless steel yarn electrodes

3 healthy subjects

Static postures of the hand and wrist.

EMG readings from e-textile.

Tasks classified with accuracy of 89.1 % +/- 1.9 %

Yang et al., 2014, [34]

Prototype design and validation

Design screen- printed fabric electrode array to stimulate muscle.

Multi- layer screen printed electrodes.

2 healthy individuals

E-textile/PCB array stimulated to produce hand postures.

Electrogoniometer

E-textile >90 % of movt generated by PCB array. E-textile greater repeatability.