Table 2.

Identification of motor impairment and activity limitation.

Author	Participants	UE disability	Sensor information	Data collection	Data processing	Data analysis	Results
Chaeibakhsh et al. [40]	N=8 stroke, acute	FMA = 4–53	Brand: ADPM Opal IMU Number: 5 Placement: sternum, bilateral wrist, bilateral arm	Inpatient: FMA items	Labeling: video Preprocessing: band-pass filtering	Model type: ML, decision tree algorithm and bagging forest algorithm Training: supervised Classification: movement categories – synergy, out of synergy, wrist/hand function, fine motor coordination (based on FMA scores) Validation: out-of-subject testing	Error rate: 0.03 ± 0.03 bagging forest 0.18 ± 0.01 decision tree
Datta et al. [48]	n=15 control n=32 stroke, acute	NIHSS 1–4	Brand: Eoxys, ACC Number: 2 Placement: bilateral wrist	Acute: finger tapping and swiping, hand opening/closing, wrist torsion, elbow flexion and extension, 3 min of spontaneous movement	Labeling: NIHSS scores (control, moderate, severe) Processing: band-pass filtering, marker-based segmentation for structured and spontaneous motion	Model type: ML, hierarchical discriminant analysis, AUC and ROC (classification performance) Training: supervised Classification: healthy or stroke, moderate or severe Validation: out-of-subject testing	Sensitivity: 0.87 overall 0.91–0.93 healthy or stroke 0.82–0.86 moderate or severe Accuracy: 0.91 overall 0.92 healthy or stroke 0.87–0.96 moderate or severe
Datta et al. [49]	n=15 control n=40 stroke, acute	NIHSS 1–4	Brand: Eoxys, ACC Number: 2 Placement: bilateral wrist	Acute: finger tapping and swiping, hand opening/closing, wrist torsion, elbow flexion and extension, 3 min of spontaneous movement	Labeling: NIHSS scores (control, mild, moderate, severe) Processing: band-pass filtering, segmentation for short-term activity	Model type: ML, hierarchical discriminant analysis, the Kruskal–Wallis test (feature extraction), AUC and ROC (classification performance) Training: supervised Classification: healthy or stroke, mild or moderate-to-severe, moderate or severe, mild-to-moderate or severe Validation: out-of-subject testing	Sensitivity: 0.78 overall 0.87 healthy or stroke 0.80 mild or moderate-to-severe 0.88 moderate or severe 0.92 mild-to-moderate or severe Accuracy: 0.78 overall 0.94 healthy or stroke 0.80 mild or moderate-to-severe 0.95 moderate or severe 0.87 mild-to-moderate or severe
Gubbi et al. [38]	n=7 control n=15 stroke, acute	Not specified	Brand: Crossbow iMote2 ACC Number: 2 Placement: bilateral wrist	Inpatient: non-standardized motion – 4h immediately post stroke, 1 h after day 1	Labeling: not specified Preprocessing: band-pass filtering	Model type: non-ML, threshold-based algorithm using cross correlation of ACC magnitude and difference in cross correlation of ACC magnitude of 3 axes Training: N/A Classification: impaired or not impaired Validation: N/A	Sensitivity: 0.87 cross correlation of ACC magnitude 0.95 correlation of 3 axes
Gubbi et al. [41]	n=10 control n=15 stroke, acute	NIHSS >0	Brand: Crossbow iMote2 ACC Number: 2 Placement: bilateral wrist	Inpatient: non-standardized motion – 4h immediately post stroke, 1 h after day 1	Labeling: not specified Preprocessing: band-pass filtering	Model type: non-ML, threshold-based algorithm correlation of activity indices to NIHSS motor score Training: N/A Classification: NIHSS motor score Validation: N/A	Accuracy: 0.72 norm-based 0.73 SMA-based 0.81 energy-based
Heron et al. [37]	n=10 control n=30 stroke, acute	NIHSS, motor UE = 2 (median)	Brand: Crossbow iMote2 ACC Number: 2 Placement: bilateral wrist	Inpatient: non-standardized motion – 5 h over 25 h period	Labeling: not specified Preprocessing: band-pass filtering	Model type: non-ML, threshold-based algorithm, ICC, ROC curve analysis (diagnostic threshold) Training: N/A Classification: impaired or not impaired Validation: N/A	Sensitivity: 0.95 NIHSS and ICC correlation: −0.53, p = 0.02 Spearman’s rho
Lin et al. [43]	n=15 control n=15 stroke, chronicity not specified	Brunnstrom levels 4–6	Brand: custom built IMU glove Number: 16 Placement: affected hand	Outpatient: grasp and release, thumb task, card turning	Labeling: Brunnstrom levels Preprocessing: band-pass filtering	Model type: ML, K-means clustering, K-fold cross validation algorithm, twofold, 10-fold, and leave-one-out Training: supervised Classification: Brunnstrom categories 4, 5, 6 Validation: out of subject testing	Accuracy: 0.73 B6 (healthy), 0.70 B5, 0.75 B4
Lin et al. [39]	n=15 control n=15 stroke, chronicity not specified	Brunnstrom levels >3	Brand: custom built IMU glove Number: 16 Placement: affected hand	Outpatient: thumb task, grip task, card-turning	Labeling: Brunnstrom levels Preprocessing: band-pass filtering	Model type: ML, logistic regression, principle component analysis (feature extraction), AUC and ROC (classification performance) Non-ML, the Kruskal–Wallis test Training: supervised Classification: impaired or not impaired, Brunnstrom levels 4, 5 and healthy subject (non-ML, the Kruskal–Wallis test) Validation: not specified	Sensitivity, impaired or not impaired: 0.98
Lucas et al. [47]	N=4 stroke, acute	Oxford Motor Scale, 0–5	Brand: Axivity AX3, ACC Number: 4 Placement: bilateral wrists and ankles	Inpatient: non-standardized motion for duration of ICU stay (up to 14 days)	Labeling: Oxford Motor Scale scores 0–2 = dependent 3–5 = antigravity Preprocessing: band-pass filtering	Model type: ML, support vector machines Training: Supervised Classification: dependent or antigravity UE Validation: out-of-subject testing	Sensitivity: 0.87 Accuracy: 0.82
Parnandi et al. [44]	N=1 stroke, chronicity not specified	Not specified	Brand: IMU custom built Number: 1 Placement: affected wrist	Outpatient: 15 WMFT tasks	Labeling: video Preprocessing: band-pass filtering	Model type: ML, naïve Bayesian classifier Training: supervised Classification: WMFT-FAS score Validation: not specified	RMS value (error estimate): 0.45
Patel et al. [45]	N = 24 stroke, chronicity not specified	WMFT-FAS = 47.2 (mean)	Brand: not specified, ACC Number: 6 Placement: Index finger, thumb, trunk, hand, forearm, upper arm	Outpatient: 8 WMFT tasks – 4 reaching, 4 manipulation	Labeling: digital markers, performance videotaped Preprocessing: band-pass filtering	Model type: ML, random forest, RRelief algorithm and Davies Bouldin index (feature selection) Training: supervised Classification: WMFT-FAS score Validation: not specified	RMS value (error estimates): 0.056 for 8 tasks 0.091 for 4 tasks
Tang et al. [50]	N=59 stroke, n=26 subacute, n = 33 chronic	Not specified	Brand: Axivity AX3, ACC Number: 2 Placement: bilateral wrist	Setting not specified: 9 CAHAI items, non-standardized motion of 3-day period for 8 weeks	Labeling: not specified Processing: band-pass filtering, day time activity only (9 am–9 pm)	Model type: ML, Gaussian Mixture Model and principle component analysis (feature clustering and extraction), linear mixed effects model, non-linear mixed effects model with Gaussian process (NMM-GP) Training: supervised Classification: CAHAI scores Validation: out-of-subject testing	RMS value (error estimates) for NMM-GP: 6.9 (subacute stroke) 3.4 (chronic stroke)
Yu et al. [46]	N=24 stroke, subacute and chronic	FMA = 18 (mean)	Brand: Analog Devices ACC, not specified, flex sensors Number: 2 (ACC), 7 (flex sensors) Placement: forearm, upper arm (ACC); wrist and hand (flex sensors)	Outpatient: 7 FMA items – 4 proximal and 3 distal 10x Home: 7 FMA items every week for 3 months (n=5)	Labeling: FMA scores Preprocessing: band-pass filtering	Model type: ML, Extreme Learning Machine algorithm (feature extraction and selection), RRelief algorithm (feature selection refinement) Training: supervised Classification: FMA scores for each item Validation: out-of-subject testing.	Coefficient of determination, overall: 0.92 (R2)
Zhang et al. [42]	n=9 control n=21 stroke, chronicity not specified	Brunnstrom levels ≥3	Brand: InvenSense IMU Number: 1 Placement: wrist	Setting not specified: single shoulder touching task completed 5x	Labeling: Brunnstrom levels Preprocessing: band-pass filtering	Model type: ML, constrained dynamic time warping algorithm, Naïve Bayes, quadratic discriminant analysis, K-nearest neighbor (KNN) Training: supervised Classification: Brunnstrom levels 3, 4, 5, 6 Validation: out-of-subject testing	Sensitivity (most accurate using KNN): 0.93 B3, 0.88 B4, 0.80 B5, 0.93 B6, 0.82 overall

ACC: accelerometer; B4–6: Brunnstrom stage 4–6; AUC: area under the curve; CAHAI: Chedoke Arm and Hand Activity Inventory; FMA: Fugl-Meyer Assessment; ICC: intra-class correlation; IMU: inertial measurement unit; ML: machine learning; N/A: not applicable; NIHSS: National Institute of Health Stroke Scale; non-ML: non-machine learning; ROC: receiver operating characteristic; RMS: root mean square; WMFT-FAS: Wolf Motor Function Test, Functional Ability Scale.

Participants: number of healthy and/or stroke participants, level of chronicity (acute: onset – 1 month, subacute 1–6 months, chronic >6 months); UE disability: motor impairment (Brunnstrom stages, FMA, NIHSS, Oxford Scale) or activity limitation (WMFT-FAS); sensor information: name and type of device, number and location of sensors placed on the UE; data collection: location of data collection, type of UE motion collected; data processing: methods for data labeling and preprocessing, and wireless data transfer for real time processing (if applicable); data analysis: model type (machine learning or non-machine learning), training type (supervised/unsupervised for machine learning models), classification of UE motion (presence/absence of impairment, motor impairment, or activity limitation levels), and validation of algorithm (out-of-subject, in-subject testing for machine learning models); results: reported outcomes vary based on machine learning or non-machine learning approach (sensitivity, accuracy, error estimates).