Direct Rehabilitation System
Using Probabilistic Neural Networks*
Kensuke Takenaka† , Ryoma Matsumae† , Keisuke Shima† and Koji Shimatani‡† Graduate School of Environment and Information Sciences, Yokohama National University, Yokohama, Japan

Abstract—In this paper, we propose a joint movement transmission system using FES based on a parallel-type discrimination
structure for electromyography (EMG) signals to support effective rehabilitation. In experiments, we conducted the intervention
using the proposed system and quantitatively evaluated theeffect of functional reconstruction. The results showed that theproposed system improved motor function and reduced joint
pain and muscle tension, suggesting that it may be an effective
rehabilitation system for paralyzed patients.
Index Terms—functional electrical stimulation, motion estimation, neural networks, AI-based rehabilitation system

I. I NTRODUCTION
Various rehabilitation programs have been provided tostroke patients to reconstruct the upper limb functions required to return to daily life and social activities. However,the provision of verbal or manual guidance by therapists
is challenging, underscoring the necessity for objective andquantitative assessment and intervention strategies [1].
To solve these problems, rehabilitation using robots andfunctional electrical stimulation (FES) has been proposed [1].
In rehabilitation using FES, peripheral nerves are electricallystimulated to induce muscle contraction and patient movement.
In addition, we proposed a human-to-human joint motion
transmission system using FES and electromyography (EMG)
signals [2]. This system supports rehabilitation by intuitively
communicating and teaching the joint motion and muscle contraction state by estimating the motion based on EMG signals
and providing an FES that reproduces the motion. However,
because this method extracts global amplitude informationfrom the EMG signal for identification, the responsiveness ofthe motion transmission is reduced and the user’s sense ofagency may be impaired.
In this paper, we focus on the electromechanical delay of theEMG signal and propose a discrimination model that connects
a transition state discriminator that uses the waveform lengthof the signal as a feature and a steady state discriminator thatuses amplitude information in a stepwise manner. This model
enables fast and stable motion transfer, and effective training
can be performed without impairing the user’s sense of agency.
* This work was supported by JSPS Kakenhi Grant Number 23H03242.

Corresponding Author: Kensuke Takenaka.
Email: takenaka-kensuke-rc@ynu.jp

EMG signal

Measure signals‡ Faculty of Health and Welfare, Prefectural University of Hiroshima, Mihara, Japan

Signal processing

Parallel typeclassification model
Length info.

Power info.

Electrical stimulation

FES controller

Motion detection

Fig. 1. Concept of the EMG-driven FES rehabilitation system.

II. P ROPOSED SYSTEM
The concept of the proposed EMG-driven FES rehabilitation
system is illustrated in Fig. 1. EMG signals are measured andsmoothed amplitude patterns and waveform length patterns arecalculated as feature vectors. The user’s motions are probabilistically estimated using probabilistic neural networks, andmotion transfer is realized by applying electrical stimulation
to the muscle group to induce the estimated motion.
A. Signal processing and feature extraction
The EMG signals measured from I pairs of electrodes
attached to the user are A/D converted (sampling frequency: fsHz), and Ei (t) is obtained using a second-order Butterworth
bandpass filter (cutoff frequency: fclow , fchigh Hz) to extractthe effective frequency components of the EMG signals. Next,
each signal is full-wave rectified and smoothed by a secondorder low-pass Butterworth filter (cutoff frequency: fc ) tocompute Ei′ . Then, Ei′st (t) at rest is excluded, and Einorm (t)is determined by normalizing with the maximum value ofeach channel. The smoothed amplitude pattern xa (t) =[xa1 (t), xa2 (t), . . . , xai (t), . . . , xaI (t)]T is calculated by normalizing Einorm (t) so that all channels sum to 1.
The sum of the window width M samples for the distance
between two points Di (t) = |Ei (t) − Ei (t − 1)| is calculated
as the waveform length Ti (t). Then, the length is normalized
by the maximum value to obtain Tinorm (t). The waveform
length pattern xl (t) = [xl1 (t), xl2 (t), . . . , xli (t), . . . , xlI (t)]Tis calculated by taking the maximum value of N samples of
the window width for the Ti (t).
B. Motion estimation
In motion estimation, discrimination is conducted using atransition state discriminator using a waveform length patternxl (t) and a stationary state discriminator using a smoothed
amplitude pattern xa (t). The transition state discriminatorconsists of a probabilistic neural network with I-dimensional
input and C +1-dimensional output, and a posterior probability
vector Ptran of C-class motion and rest class is calculated
from the transition of the initial EMG signal by inputtingxl (t). On the other hand, the steady state discriminator consists of a probabilistic neural network with I-dimensional input
and C-dimensional output, and the posterior probability vector
Pst of the C class motion is calculated by inputting xa (t).
The posterior probabilities are combined by the transition time
tm and an arbitrary pre-determined steady state transition time
α, and the model posterior probability P (t) as follows:(Ptran (t) if tm ≥ α,(1)
P (t) =Pst (t)if tm < α,where the transition time tm is increased when the transitionstate discriminator is discriminated as an arbitrary motion classand takes the value 0 when it is discriminated as a resting class.
The maximum class of posterior probabilities P (t) is estimated as the movement performed by the user, and the joint
movement is realized by applying FES to the muscle groupthat induces the corresponding movement. This enables fastand stable motion transfer and effective training for the user.
III. E XPREMENTS
The number of subjects in the experiment was five, but
due to space limitations, this paper details the results of theintervention for a stroke patient in his 70s. The subjects had
been left hemiplegic for 4.5 years and had contractures of theleft hand joints and fingers.
The EMG signals were measured using an integrated measurement device Sensor I/F SI1000 (Oisaka Electronic Equipment Ltd.) and a wet two-pole electrode, which was applied
to the skin surface of the flexor carpi radialis and extensor
carpi ulnaris muscles on the non-paralyzed side (I = 2). The
sampling frequency was set to 1000 [Hz], and the parametersof the proposed signal processing method were fclow = 1[Hz],fchigh = 250[Hz], fc = 1[Hz], M = 80, N = 80. Stimulationelectrodes were applied to the subjects by trial and error,
based on anatomical knowledge of the positions of the target
muscles, to induce flexion and extension. Three discrimination
classes were defined and estimated: hand flexion, hand extension, and resting state using probabilistic neural networks [3].
The Modified Ashworth Scale (MAS), an evaluation methodof muscle tone, and the Fugl-Meyer Assessment (FMA) for
upper limb scores, a comprehensive evaluation method ofmotor function impairment, were measured in the subjects
before and after the intervention. Training consisted of 15
minutes of training with the proposed system two days a week
for 12 weeks. Figure 2 shows the scenes during training. The
subject is able to perform bilateral movements triggered by hisown movement, since the movement of the non-paralyzed side
is transmitted to the paralyzed side by electrical stimulation.
The experiments were conducted under the approval of theEthics Committee of Fukuyama Memorial Hospital (ApprovalNo. 20231001).

SubjectFig. 2. An example of the experimental scene.
2560PrePostMuscle Group

5020401530PrePost

Shoulder flexors

00

Shoulder extensors

11Shoulder abductors

10Shoulder adductors

0020

Elbow flexors

00

510

Elbow extensors

1100

Wrist flexors

00

Wrist extensors

1+1+

Finger flexors

00

Finger extensors

1+

1

10

A-DHIJTotal

A-D: Motor function
H: Sensation
I: Passive joint motion J: Joint pain(a) FMA(b) MAS

Fig. 3. Clinical scores measured before and after intervention.

Figure 3(a) presents the outcomes of FMA conducted preand post assessments. The results showed that all scores formotor function (A-D), sensation (H), passive joint motion (I),and joint pain (J) were improved by the intervention. The
major improvement was seen in joint pain (J), with the total
upper extremity score increasing from 35/126 to 57/126. TheFES intervention is known to reduce upper extremity pain [4],and the proposed method may have had a similar effect.
The MAS results for the pre and post assessments are shownin Figure 3(b). The results show that the scores for shoulder
abductors and hand extensors improved. In particular, handextension is a movement induced by the FES, suggesting thatmuscle tone may have decreased due to the external inductionof muscle contraction.
IV. C ONCLUSION
In this paper, we proposed a FES rehabilitation system basedon motion estimation. The experimental results showed thatthe proposed system improved motor function and reduced
joint pain and muscle tension, suggesting that it could bean effective rehabilitation system. In future studies, we plan
to increase the number of subjects and conduct a detailedinvestigation of the rehabilitation effects of the proposedsystem by comparing it with other rehabilitation methods.
R EFERENCES[1] A. Pollock, S. E. Farmer, M. C. Brady, P. Langhorne, G. E. Mead,
J. Mehrholz, and F. van Wijck, “Interventions for improving upper limb
function after stroke,” Cochrane Database of Systematic Reviews, no. 11,
p. CD010820, 2014.
[2] K. Shima and K. Shimatani, “A new approach to direct rehabilitation
based on functional electrical stimulation and EMG classification,” in2016 International Symposium on Micro-NanoMechatronics and Human
Science (MHS), Jan. 2016, pp. 1–6.
[3] T. Tsuji, N. Bu, O. Fukuda, and M. Kaneko, “A recurrent log-linearized
Gaussian mixture network,” Ieee Transactions on Neural Networks,
vol. 14, no. 2, pp. 304–316, Mar. 2003.
[4] W. Silva, G. Cirne, E. Silva-Filho, E. Cacho, J. M. Lopes, R. Cacho,and M. Baroni, “Functional electrical stimulation reduces pain andshoulder subluxation in chronic post-stroke patients?” Manual Therapy,Posturology & Rehabilitation Journal, pp. 1–5, July 2020.