Table 1.

Identification of functional motion.

Author	Participants	UE disability	Sensor information	Data collection	Data processing	Data analysis	Results
Biswas et al. [27]	n=4 control n=4 stroke, chronicity not specified	Not specified	Brand: Shimmer IMU Number: 1 Placement: affected wrist	Outpatient: “make cup of tea” activity consisting of reach and retrieve object, drinking, and pouring	Labeling: observation notes Preprocessing: band-pass filtering of ACC data only	Model type: non-ML, rule-based algorithm to determine sensor orientation, position transitions, and sequence detection Training: N/A Classification: functional movements (reach and retrieve object, drinking, pouring) Validation: N/A	Accuracy: 0.91–0.99 control 0.70–0.85 stroke
Biswas et al. [28]	n=4 control n=4 stroke, chronicity not specified	Not specified	Brand: Shimmer IMU Number: 1 Placement: affected wrist	Outpatient: reach and retrieve, drinking, pouring water	Labeling: observation notes Preprocessing: band-pass filtering	Model type: ML, supervised K means clustering Training: supervised Classification: functional movements (reach and retrieve object, drinking, pouring) Validation: out-of-subject testing	Accuracy: 0.88 control (ACC) 0.83 control (gyroscope) 0.70 stroke (ACC) 0.66 stroke (gyroscope)
Bochniewicz et al. [33]	n=10 control n=10 stroke, chronic	ARAT = 5–41	Brand: Analog Devices IMU Number: 1 Placement: affected UE	Outpatient: (mock apartment) laundry, shopping, kitchen activity, and making the bed	Labeling: video footage Preprocessing: band-pass filtering	Model type: ML, random forest Training: supervised Classification: functional vs. non-functional movement Validation: in-subject testing (intrasubject approach), and out-of-subject testing (inter-subject approach)	Accuracy: 0.91 control 0.70 stroke
Guerra et al. [32]	n=10 control n=6 stroke, chronic	FMA = 52.8 (mean)	Brand: XSens IMU Number: 11 Placement: head, sternum, pelvis, scapulae, BUEs	Outpatient: tabletop and shelf reaching tasks, feeding task, drinking task	Labeling: video footage Preprocessing: band-pass filtering	Model type: ML, sliding window approach, hidden Markov model, and logistical regression Training: supervised Classification: functional primitives (rest, reach, transport, retract) Validation: out-of-subject testing	Sensitivity: 0.82 control 0.75 stroke
Lemmens et al. [26]	n=30 control n=1 stroke, chronicity not specified	Not specified	Brand: Shimmer IMU Number: 7 Placement: bilateral hand, wrist, upper arm, and chest	Outpatient: drinking, eating, hair brushing Home: 30 min daily life recording (control, n=1)	Labeling: time borders of summed gyroscope data Preprocessing: band-pass filtering	Model type: non-ML, threshold-based approach, template matching algorithm using 2D convolution Training: N/A Classification: activity and functional movement (drinking, eating, hair brushing) Validation: out of subject testing	Sensitivity: 1.0 control 1.0 stroke Sensitivity, drinking: 1.0 control (non-standardized)
Lum et al. [34]	n=10 control n=10 stroke, chronic	ARAT = 23.5 (mean)	Brand: custom built IMU Number: 2 Placement: bilateral wrist	Outpatient: (mock apartment) laundry, shopping, kitchen activity, and making the bed	Labeling: video footage (functional, non-functional, unknown) Preprocessing: band-pass filtering	Model type: ML, random forest, radial basis function support vector machine Training: supervised Classification: functional vs. non-functional movement Validation: in-subject testing (intrasubject approach), and out-of-subject testing (inter-subject approach)	Accuracy, intra-subject approach 0.96 control 0.93 stroke Accuracy, inter-subject approach: 0.91 controls 0.74 stroke
Mazomenos et al. [29]	n=18 control n=4 stroke, chronicity not specified	Not specified	Brand: Shimmer IMU Number: 2 Placement: proximal affected wrist and elbow	Outpatient: reach and retrieve, pouring water, drinking	Labeling: pre-defined start/end positions during data collection Preprocessing: band-pass filtering	Model type: non-ML, rule-based detection algorithm to recognize kinematic patterns Training: N/A Classification: functional movement (reach and retrieve, drinking, pouring) Validation: N/A	Accuracy: 0.89–0.96 control 0.84–0.93 stroke
Panwar et al. [31]	N=14 stroke, chronic	Not specified	Brand: Shimmer IMU and Physilog IMU Number: 1 (outpatient), 3 (home) Placement: affected wrist (outpatient), bilateral wrist and sternum (home)	Outpatient: “make cup of tea” activity consisting of reach and retrieve, lift arm, rotate arm Home: 2 continuous hours during weekday, encouraged to use UE for daily activities (non-standardized)	Labeling: observational notes Data augmentation Preprocessing: band-pass filtering, signal length standardized, signal transformation	Model type: ML, convolutional neural network (deep learning), 10-fold cross validation Training: supervised Classification: functional movements (reach and retrieve object, drinking, pouring) Validation: Out-of-subject testing	Accuracy, 0.98 0.89 non-standardized condition
Roy et al. [30]	N=10 stroke, chronic	Brunnstrom levels 3–5	Brand: Analog Devices ACC Number: 8 (ACC), 8 (sEMG) Placement: thigh, trunk, bilateral shoulder, bilateral forearm	Outpatient: 11 ADL activities based on the FIM in the areas of: feeding, grooming, dressing, functional mobility	Labeling: not specified Preprocessing: band-pass filtering	Model type: ML, artificial neural network, adaptive neuro-fuzzy adaptive system, scaled conjugate gradient algorithm Training: supervised Classification: functional movements (feeding, brushing teeth, hair combing, buttoning shirt, sit to stand, walking, toileting) Validation: out-of-subject testing	Sensitivity: 0.95 ACC+sEMG 0.93 ACC Specificity: 0.99 ACC+sEMG 0.97 ACC
Zambrana et al. [35]	n=15 control, n=6 stroke, chronicity not specified	Not specified	Brand: BNO055 IMU Number: 2 Placement: bilateral wrist	Outpatient: eating, pouring water, drinking, brushing teeth, folding a towel, walking	Labeling: video footage Preprocessing: band-pass filtering	Model type: module 1 – non-ML, threshold-based analysis module 2 – ML, K-nearest neighbor, support vector machines, random forest Training: supervised Classification: module 1 – movement vs. non-movement module 2 – functional vs. non-functional movement Validation: in-subject testing (intrasubject approach), and out-of-subject testing (inter-subject approach)	Accuracy, module 1: 0.89 Accuracy, module 2: 0.91 intra-subject control 0.90 inter-subject control 0.61 stroke

ACC: accelerometer; IMU: inertial measurement unit; FIM: Functional Independence Measure; FMA: Fugl-Meyer Assessment; ML: machine learning; non-ML: non-machine learning; sEMG: surface electromyography. 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 (FMA, Brunnstrom stages) or activity limitation (ARAT); 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; data analysis: model type (machine learning or non-machine learning), training (supervised/unsupervised), classification of UE motion (activity, functional movement, functional primitive, functional vs. non-functional), and validation of the algorithm (out-of-subject, in-subject testing); results: performance of algorithm (sensitivity or accuracy), unless otherwise reported, results reflect out-of-subject testing and data collected under standardized conditions (controlled environment with pre-set tasks).