diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst
index b02439d26..83858db13 100644
--- a/docs/api/datasets.rst
+++ b/docs/api/datasets.rst
@@ -243,5 +243,6 @@ Available Datasets
     datasets/pyhealth.datasets.ClinVarDataset
     datasets/pyhealth.datasets.COSMICDataset
     datasets/pyhealth.datasets.TCGAPRADDataset
+    datasets/pyhealth.datasets.DSADataset
     datasets/pyhealth.datasets.splitter
     datasets/pyhealth.datasets.utils
diff --git a/docs/api/datasets/pyhealth.datasets.DSADataset.rst b/docs/api/datasets/pyhealth.datasets.DSADataset.rst
new file mode 100644
index 000000000..84e909b07
--- /dev/null
+++ b/docs/api/datasets/pyhealth.datasets.DSADataset.rst
@@ -0,0 +1,10 @@
+pyhealth.datasets.DSADataset
+===============================
+
+The Daily and Sports Activities (DSA) dataset contains motion sensor data of 
+daily and sports activities. For more information see `Daily and Sports Activities <https://archive.ics.uci.edu/dataset/256/daily+and+sports+activities>`_. This dataset was contributed as part of the Prostate-VarBench benchmarking work (`arXiv:2511.09576 <https://arxiv.org/abs/2511.09576>`_).
+
+.. autoclass:: pyhealth.datasets.DSADataset
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/api/tasks.rst b/docs/api/tasks.rst
index 399b8f1aa..43a3f8802 100644
--- a/docs/api/tasks.rst
+++ b/docs/api/tasks.rst
@@ -229,3 +229,4 @@ Available Tasks
     Mutation Pathogenicity (COSMIC) <tasks/pyhealth.tasks.MutationPathogenicityPrediction>
     Cancer Survival Prediction (TCGA) <tasks/pyhealth.tasks.CancerSurvivalPrediction>
     Cancer Mutation Burden (TCGA) <tasks/pyhealth.tasks.CancerMutationBurden>
+    Activity Classification (DSA) <tasks/pyhealth.tasks.ActivityClassification>
diff --git a/docs/api/tasks/pyhealth.tasks.ActivityClassification.rst b/docs/api/tasks/pyhealth.tasks.ActivityClassification.rst
new file mode 100644
index 000000000..83ded0401
--- /dev/null
+++ b/docs/api/tasks/pyhealth.tasks.ActivityClassification.rst
@@ -0,0 +1,7 @@
+pyhealth.tasks.ActivityClassification
+===================================
+
+.. autoclass:: pyhealth.tasks.ActivityClassification
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/examples/dsa_activity_classification.py b/examples/dsa_activity_classification.py
new file mode 100644
index 000000000..afbabe060
--- /dev/null
+++ b/examples/dsa_activity_classification.py
@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+"""dsa_activity_classification.ipynb
+
+# Activity Classification Using DSA Dataset
+
+# Install PyHealth
+
+You might need to restart kernel after running this section.
+"""
+
+!git clone https://github.com/ranyou/PyHealth.git
+!cd PyHealth && pip install -e .
+
+"""# Load Dataset"""
+
+from pyhealth.datasets import DSADataset
+
+dataset = DSADataset(download=True, root="./daily-and-sports-activities")
+dataset.stats()
+
+"""# Define Task"""
+
+samples = dataset.set_task()
+
+from pyhealth.datasets import get_dataloader, split_by_sample
+
+train_dataset, val_dataset, test_dataset = split_by_sample(samples, [0.7, 0.1, 0.2])
+
+train_loader = get_dataloader(train_dataset, batch_size=16, shuffle=True)
+val_loader = get_dataloader(val_dataset, batch_size=16, shuffle=False)
+test_loader = get_dataloader(test_dataset, batch_size=16, shuffle=False)
+
+"""# Define Model"""
+
+from pyhealth.models import RNN
+
+model = RNN(samples)
+
+"""# Train Model"""
+
+from pyhealth.trainer import Trainer
+
+trainer = Trainer(model=model, metrics=["accuracy"])
+trainer.train(train_dataloader=train_loader, val_dataloader=val_loader, epochs=1)
+
+"""# Evaluate Model"""
+
+trainer.evaluate(test_loader)
+
diff --git a/pyhealth/datasets/__init__.py b/pyhealth/datasets/__init__.py
index 54e77670c..01da8c066 100644
--- a/pyhealth/datasets/__init__.py
+++ b/pyhealth/datasets/__init__.py
@@ -53,6 +53,7 @@ def __init__(self, *args, **kwargs):
 from .cosmic import COSMICDataset
 from .covid19_cxr import COVID19CXRDataset
 from .dreamt import DREAMTDataset
+from .dsa import DSADataset
 from .ehrshot import EHRShotDataset
 from .eicu import eICUDataset
 from .isruc import ISRUCDataset
diff --git a/pyhealth/datasets/configs/dsa.yaml b/pyhealth/datasets/configs/dsa.yaml
new file mode 100644
index 000000000..54bc5d4cc
--- /dev/null
+++ b/pyhealth/datasets/configs/dsa.yaml
@@ -0,0 +1,136 @@
+version: "1.0"
+tables:
+  activities:
+    file_path: "dsa-pyhealth.csv"
+    patient_id: "patient"
+    timestamp: null
+    attributes:
+    - "activity"
+    - "patient"
+    - "segment"
+    - "sensor"
+    - "0"
+    - "1"
+    - "2"
+    - "3"
+    - "4"
+    - "5"
+    - "6"
+    - "7"
+    - "8"
+    - "9"
+    - "10"
+    - "11"
+    - "12"
+    - "13"
+    - "14"
+    - "15"
+    - "16"
+    - "17"
+    - "18"
+    - "19"
+    - "20"
+    - "21"
+    - "22"
+    - "23"
+    - "24"
+    - "25"
+    - "26"
+    - "27"
+    - "28"
+    - "29"
+    - "30"
+    - "31"
+    - "32"
+    - "33"
+    - "34"
+    - "35"
+    - "36"
+    - "37"
+    - "38"
+    - "39"
+    - "40"
+    - "41"
+    - "42"
+    - "43"
+    - "44"
+    - "45"
+    - "46"
+    - "47"
+    - "48"
+    - "49"
+    - "50"
+    - "51"
+    - "52"
+    - "53"
+    - "54"
+    - "55"
+    - "56"
+    - "57"
+    - "58"
+    - "59"
+    - "60"
+    - "61"
+    - "62"
+    - "63"
+    - "64"
+    - "65"
+    - "66"
+    - "67"
+    - "68"
+    - "69"
+    - "70"
+    - "71"
+    - "72"
+    - "73"
+    - "74"
+    - "75"
+    - "76"
+    - "77"
+    - "78"
+    - "79"
+    - "80"
+    - "81"
+    - "82"
+    - "83"
+    - "84"
+    - "85"
+    - "86"
+    - "87"
+    - "88"
+    - "89"
+    - "90"
+    - "91"
+    - "92"
+    - "93"
+    - "94"
+    - "95"
+    - "96"
+    - "97"
+    - "98"
+    - "99"
+    - "100"
+    - "101"
+    - "102"
+    - "103"
+    - "104"
+    - "105"
+    - "106"
+    - "107"
+    - "108"
+    - "109"
+    - "110"
+    - "111"
+    - "112"
+    - "113"
+    - "114"
+    - "115"
+    - "116"
+    - "117"
+    - "118"
+    - "119"
+    - "120"
+    - "121"
+    - "122"
+    - "123"
+    - "124"
\ No newline at end of file
diff --git a/pyhealth/datasets/dsa.py b/pyhealth/datasets/dsa.py
new file mode 100644
index 000000000..57b414afc
--- /dev/null
+++ b/pyhealth/datasets/dsa.py
@@ -0,0 +1,198 @@
+"""Daily and Sports Activities dataset for PyHealth.
+
+This module provides the DSADataset class for loading and processing
+DSA (Daily and Sports Activities) data for machine learning tasks.
+"""
+
+import logging
+import os
+from pathlib import Path
+from typing import List, Optional
+
+import pandas as pd
+
+from .base_dataset import BaseDataset
+
+logger = logging.getLogger(__name__)
+
+
+class DSADataset(BaseDataset):
+    """Daily and Sports Activities dataset for motion sensor data analysis.
+
+    Daily and Sports Activities (DSA) dataset contains motion sensor data of 19
+    daily and sports activities each performed by 8 subjects for 5 minutes in 
+    their own style. The 5-min period is divided into 5-sec segments so that 60 
+    segments are obtained for each activity and each subject. 
+    
+    Five sensor units are placed on the torso, arms, and legs to capture 
+    time-series data. Sensors are configured to capture data at 25 Hz frequency. 
+
+    In each text file, there are 45 columns (5 units * 9 sensors) and 125 rows 
+    (5 seconds * 25 Hz). Each row represents sensor dimensions captured at a p
+    articular sampling instant from all sensors for one activity, one patient.
+
+    The raw dataset structure looks like this: 
+    - <root>
+        - data
+            - a01: activity 01
+                - p1: patient 1
+                    - s01.txt: segment 01
+                    - s02.txt: segment 02
+                    - ... ...
+                    - s60.txt: segment 60
+                - p2: patient 2
+                - ... ...
+                - p8
+            - a02: activity 02
+            - ... ...
+            - a19
+
+    This dataset class loads raw data files and reformats into a single CSV file
+    with columns activity, patient, segment, sensor, and 0-124 columns that
+    represent time series.
+
+    Dataset is available at:
+    https://archive.ics.uci.edu/dataset/256/daily+and+sports+activities
+
+    Note:
+        This dataset is licensed under a Creative Commons Attribution 4.0 
+        International (CC BY 4.0) license.
+
+    Args:
+        root: Root directory of the raw data containing the DSA dataset files.
+        dataset_name: Optional name of the dataset. Defaults to "dsa".
+        config_path: Optional path to the configuration file. If not provided,
+            uses the default config in the configs directory.
+
+    Attributes:
+        root: Root directory of the raw data.
+        dataset_name: Name of the dataset.
+        config_path: Path to the configuration file.
+
+    Examples:
+        >>> from pyhealth.datasets import DSADataset
+        >>> dataset = DSADataset(root="/path/to/dsa")
+        >>> dataset.stats()
+        >>> samples = dataset.set_task()
+        >>> print(samples[0])
+    """
+
+    def __init__(
+        self,
+        root: str,
+        dataset_name: Optional[str] = None,
+        config_path: Optional[str] = None,
+        download : bool = False,
+        **kwargs,
+    ) -> None:
+        if config_path is None:
+            logger.info("No config path provided, using default config")
+            config_path = Path(__file__).parent / "configs" / "dsa.yaml"
+
+        if download: 
+            self.download_dataset(root=root)
+
+        # Prepare standardized CSV if not exists
+        pyhealth_csv = os.path.join(root, "dsa-pyhealth.csv")
+        if not os.path.exists(pyhealth_csv):
+            if not os.path.exists(Path(root) / "data"): 
+                logger.info(
+                    f"""
+                    There's no data directory in {root}. You root directory is likely wrong. 
+                    You can download the dataset manually from https://archive.ics.uci.edu/dataset/256/daily+and+sports+activities
+                    then extract the downloaded zip file to <root>, or directly set download=True.
+                    """
+                )
+                raise 
+                
+            logger.info("Preparing DSA data...")
+            self.prepare_data(root)
+
+        super().__init__(
+            root=root,
+            tables=["activities"],
+            dataset_name=dataset_name or "dsa",
+            config_path=config_path,
+            **kwargs,
+        )
+
+    @staticmethod
+    def download_dataset(root : str = None) -> None: 
+        """Download DSA dataset and extract files.
+        
+        Args: 
+            root: directory to extract downloaded files, default to ./daily-and-sports-activities
+        """
+        import urllib.request
+        import zipfile
+        import os
+
+        url = "https://archive.ics.uci.edu/static/public/256/daily+and+sports+activities.zip"
+        zip_path = "./daily+and+sports+activities.zip"
+        if root is None: 
+            root = "./daily-and-sports-activities"
+
+        logger.info(f"Downloading {url}...")
+        urllib.request.urlretrieve(url, zip_path)
+
+        logger.info("Extracting files...")
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall(root)
+
+        os.remove(zip_path)
+
+        logger.info(f"Done! Files extracted to: {root}")
+        return
+
+    @staticmethod
+    def prepare_data(root: str) -> None:
+        """Prepare data for DSA dataset.
+
+        Converts raw DSA text files to standardized CSV format.
+
+        Args:
+            root: Root directory containing the DSA files.
+        """
+
+        data = []
+
+        columns = [
+            f"{x}_{z}{y}" 
+            for z in ["x", "y", "z"]
+            for y in ["acc", "gyro", "mag"]
+            for x in ["T", "RA", "LA", "RL", "LL"]
+        ]
+
+        logger.info(f"Loading raw sensors data ...")
+
+        for a in list(range(1, 20)): 
+            for p in list(range(1, 9)): 
+                for s in list(range(1, 61)): 
+                    df = pd.read_csv(Path(root) / "data" / f"a{a:02d}" / f"p{p}" / f"s{s:02d}.txt", header=None)
+                    df.columns = columns
+                    
+                    data.append(
+                        df.T.reset_index().rename(columns={"index": "sensor"}).assign(
+                            activity=a,
+                            patient=p,
+                            segment=s,
+                        )
+                    )
+                    
+        df_out = pd.concat(data)
+
+        # Save to standardized CSV
+        output_path = os.path.join(root, "dsa-pyhealth.csv")
+        df_out.to_csv(output_path, index=False)
+        logger.info(f"Saved {len(df_out)} sensors to {output_path}")
+
+    @property
+    def default_task(self):
+        """Returns the default task for this dataset.
+
+        Returns:
+            ActivityClassification: The default classification task.
+        """
+        from pyhealth.tasks import ActivityClassification
+
+        return ActivityClassification()
diff --git a/pyhealth/tasks/__init__.py b/pyhealth/tasks/__init__.py
index 797988377..71dcce08a 100644
--- a/pyhealth/tasks/__init__.py
+++ b/pyhealth/tasks/__init__.py
@@ -66,3 +66,4 @@
     VariantClassificationClinVar,
 )
 from .patient_linkage_mimic3 import PatientLinkageMIMIC3Task
+from .activity_classification import ActivityClassification
\ No newline at end of file
diff --git a/pyhealth/tasks/activity_classification.py b/pyhealth/tasks/activity_classification.py
new file mode 100644
index 000000000..45b11e315
--- /dev/null
+++ b/pyhealth/tasks/activity_classification.py
@@ -0,0 +1,98 @@
+"""Activity classification for PyHealth.
+
+This module provides a task for classifying daily and sports activity using
+motion sensor data from Daily and Sports Activities (DSA) dataset.
+"""
+
+from typing import Any, Dict, List, Optional
+import pandas as pd
+from .base_task import BaseTask
+import logging
+logger = logging.getLogger(__name__)
+
+class ActivityClassification(BaseTask):
+    """Task for classifying activity using motion sensor data.
+
+    This task classifies which activity a patient is performing over a time 
+    period from multi-sensor time-series data.
+
+    Attributes:
+        task_name (str): The name of the task.
+        input_schema (Dict[str, str]): The input schema specifying required inputs.
+        output_schema (Dict[str, str]): The output schema specifying outputs.
+
+    Note:
+        Each time-series is sampled over 5 seconds with 25 Hz frequency.
+
+    Examples:
+        >>> from pyhealth.datasets import DSADataset
+        >>> from pyhealth.tasks import ActivityClassification
+        >>> dataset = DSADataset(root="/path/to/dsa")
+        >>> task = ActivityClassification()
+        >>> samples = dataset.set_task(task)
+    """
+
+    task_name: str = "ActivityClassification"
+    input_schema: Dict[str, str] = {
+        f"{x}_{y}{z}": "sequence"
+        for x in ["T", "LA", "RA", "LL", "RL"]
+        for y in ["x", "y", "z"]
+        for z in ["acc", "gyro", "mag"]
+    }
+    output_schema: Dict[str, str] = {
+        "label": "multiclass",
+    }
+
+    def __call__(self, patient: Any) -> List[Dict[str, Any]]:
+        """Process daily and sports activities data for activity classification.
+
+        Args:
+            patient: A patient object containing activities data.
+
+        Returns:
+            List[Dict[str, Any]]: A list containing dictionaries with
+            patient features and activity label. 
+
+        Note:
+            Returns empty list for patients with:
+            - No activities
+        """
+        events = patient.get_events(event_type="activities")
+
+        if len(events) == 0:
+            return []
+        
+        df = pd.DataFrame([e.attr_dict for e in events])
+
+        def extract_time_series(df, sensor):
+            columns = [f"{d}" for d in range(125)]
+            ts = df[df["sensor"] == sensor][columns].astype(float).to_numpy().tolist()
+            # return times, values
+            if len(ts) > 0: 
+                return ts[0]
+            return []
+
+        records = []
+        for a in df["activity"].unique(): 
+            for s in df["segment"].unique(): 
+
+                df_one = df.query(f'activity == "{a}"').query(f'segment == "{s}"')
+                
+                if len(df_one) == 0: 
+                    continue
+
+                features = [
+                    f"{x}_{y}{z}" 
+                    for y in ["x", "y", "z"]
+                    for z in ["acc", "gyro", "mag"]
+                    for x in ["T", "RA", "LA", "RL", "LL"]
+                ] 
+
+                record = {
+                    feature: extract_time_series(df_one, feature) for feature in features
+                }
+                record["patient_id"] = patient.patient_id
+                record["label"] = a
+                records.append(record)
+        
+        return records
diff --git a/test-resources/dsa/dsa-pyhealth.csv b/test-resources/dsa/dsa-pyhealth.csv
new file mode 100644
index 000000000..2e7b3fe32
--- /dev/null
+++ b/test-resources/dsa/dsa-pyhealth.csv
@@ -0,0 +1,21 @@
+sensor,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,activity,patient,segment
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+T_yacc,-0.33699,-0.33723,-0.33727,-0.33686,-0.3374,-0.33684,-0.33657,-0.33691,-0.33726,-0.33702,-0.33694,-0.33673,-0.33738,-0.33611,-0.33644,-0.3374,-0.33696,-0.337,-0.337,-0.33643,-0.3372,-0.33634,-0.33693,-0.33609,-0.33787,-0.33639,-0.33698,-0.33634,-0.33617,-0.33547,-0.33696,-0.33779,-0.33664,-0.33715,-0.33624,-0.3371,-0.33632,-0.33631,-0.33577,-0.33728,-0.33738,-0.33737,-0.33665,-0.33711,-0.33782,-0.33805,-0.33735,-0.33666,-0.33769,-0.33806,-0.33857,-0.33764,-0.33693,-0.33751,-0.33701,-0.3372,-0.3374,-0.3368,-0.33642,-0.33747,-0.33819,-0.33767,-0.33737,-0.33698,-0.33826,-0.33674,-0.33606,-0.33654,-0.33527,-0.33676,-0.33709,-0.33553,-0.33659,-0.33697,-0.33619,-0.33593,-0.33657,-0.33644,-0.3371,-0.33688,-0.33694,-0.33737,-0.33657,-0.33712,-0.33656,-0.33695,-0.3371,-0.3356,-0.33763,-0.33739,-0.33769,-0.338,-0.33748,-0.33716,-0.33695,-0.33715,-0.33643,-0.33732,-0.33656,-0.33685,-0.33814,-0.33764,-0.33717,-0.33638,-0.3372,-0.33689,-0.33895,-0.33759,-0.33682,-0.33704,-0.33708,-0.3376,-0.33672,-0.33693,-0.33665,-0.33643,-0.33697,-0.33675,-0.3368,-0.3373,-0.33566,-0.33717,-0.33602,-0.33726,-0.33633,3,1,1
+LA_zmag,0.49065,0.52042,0.5518,0.56914,0.58181,0.59043,0.59569,0.60151,0.61094,0.61576,0.618,0.61737,0.61784,0.62049,0.62311,0.62609,0.62457,0.62373,0.61995,0.61126,0.59237,0.55217,0.50494,0.46486,0.42316,0.39005,0.36575,0.34613,0.32939,0.31581,0.3006,0.28935,0.28904,0.29269,0.30483,0.31908,0.3432,0.37092,0.39575,0.42805,0.46332,0.50277,0.53054,0.54031,0.54906,0.56203,0.56944,0.58352,0.59967,0.60704,0.61248,0.61231,0.61447,0.62198,0.62356,0.62688,0.6264,0.62541,0.62297,0.61096,0.59219,0.55798,0.51939,0.48353,0.44308,0.40335,0.36978,0.34453,0.32258,0.30677,0.29306,0.28553,0.27917,0.27723,0.2863,0.29894,0.31939,0.34305,0.36793,0.39727,0.42961,0.47821,0.51438,0.52337,0.52749,0.54239,0.55101,0.56293,0.58179,0.59332,0.60312,0.60726,0.61134,0.62132,0.62288,0.62112,0.61557,0.61009,0.60612,0.59401,0.57763,0.55189,0.52358,0.49957,0.4675,0.42683,0.3901,0.35995,0.33655,0.31294,0.29403,0.28469,0.27793,0.27497,0.27874,0.2837,0.29202,0.29935,0.31442,0.34458,0.38193,0.42877,0.47013,0.50793,0.54089,14,2,1
+RA_xgyro,-0.68619,-0.6868,-0.68492,-0.68523,-0.68543,-0.68596,-0.68598,-0.68598,-0.68655,-0.68506,-0.68584,-0.68502,-0.68522,-0.68466,-0.68474,-0.68512,-0.68455,-0.68501,-0.68786,-0.68598,-0.68594,-0.68567,-0.68558,-0.68467,-0.68504,-0.68432,-0.68293,-0.68444,-0.68482,-0.68478,-0.68379,-0.684,-0.68375,-0.68299,-0.68332,-0.68393,-0.68346,-0.68546,-0.68471,-0.68497,-0.6838,-0.68426,-0.68322,-0.68288,-0.68376,-0.68292,-0.6824,-0.68304,-0.68336,-0.68421,-0.68392,-0.68328,-0.68345,-0.68458,-0.68461,-0.68484,-0.68345,-0.68545,-0.68485,-0.6838,-0.6821,-0.68246,-0.68326,-0.68225,-0.68291,-0.6818,-0.68321,-0.68177,-0.68185,-0.68241,-0.68229,-0.68238,-0.68261,-0.68342,-0.68183,-0.68163,-0.68272,-0.68388,-0.68342,-0.68445,-0.6836,-0.68367,-0.68398,-0.6854,-0.68477,-0.68738,-0.68823,-0.68816,-0.69053,-0.69025,-0.69227,-0.69314,-0.69585,-0.69473,-0.69708,-0.69939,-0.70101,-0.70418,-0.70668,-0.70851,-0.71096,-0.71202,-0.71636,-0.72113,-0.72411,-0.72596,-0.72919,-0.73286,-0.73759,-0.74116,-0.74692,-0.75013,-0.75533,-0.76077,-0.76506,-0.77102,-0.77438,-0.77989,-0.7867,-0.79247,-0.79802,-0.80251,-0.80874,-0.81347,-0.81969,7,7,1
+LA_yacc,-0.78977,-0.78944,-0.78909,-0.78944,-0.78835,-0.78862,-0.78923,-0.78906,-0.78841,-0.78784,-0.78908,-0.78815,-0.78778,-0.78878,-0.78912,-0.78912,-0.78805,-0.7878,-0.78828,-0.78733,-0.78934,-0.78778,-0.78832,-0.78806,-0.78739,-0.78721,-0.78762,-0.78799,-0.78696,-0.78682,-0.78739,-0.78628,-0.78648,-0.78627,-0.78755,-0.78649,-0.78733,-0.78716,-0.78693,-0.78666,-0.78725,-0.78698,-0.78649,-0.78711,-0.78775,-0.78836,-0.78889,-0.78843,-0.78981,-0.78897,-0.78958,-0.78977,-0.78979,-0.79121,-0.79065,-0.79118,-0.79043,-0.79157,-0.79076,-0.78991,-0.79056,-0.79062,-0.78996,-0.79022,-0.78968,-0.79035,-0.79111,-0.79004,-0.78935,-0.78953,-0.78994,-0.78993,-0.79053,-0.79178,-0.79006,-0.79107,-0.7907,-0.79084,-0.79071,-0.79024,-0.79018,-0.79015,-0.79144,-0.79092,-0.79158,-0.79165,-0.79156,-0.79129,-0.79238,-0.79044,-0.79082,-0.79087,-0.79206,-0.79083,-0.7908,-0.79039,-0.78983,-0.78909,-0.79078,-0.78971,-0.78993,-0.78997,-0.79039,-0.79136,-0.79259,-0.79214,-0.79353,-0.79374,-0.79454,-0.79429,-0.79482,-0.79492,-0.79449,-0.79623,-0.7945,-0.79603,-0.79642,-0.795,-0.7939,-0.79373,-0.79547,-0.79501,-0.79476,-0.79456,-0.79348,4,2,1
+RA_zacc,0.023233,0.003854,0.019717,0.005615,0.017946,0.011798,0.002965,0.004732,-0.011997,0.012661,0.016187,0.010894,0.009125,0.000342,0.019682,0.032012,0.019707,0.009153,0.026757,0.007395,-0.000522,0.012648,0.010902,0.010044,0.010909,-0.002281,0.006501,0.016181,0.00739,0.006414,-0.006814,0.018718,0.009035,0.016971,0.003769,0.003787,0.02663,0.005502,0.019621,-0.005028,0.001991,0.024868,0.020464,-0.001524,0.010788,0.01608,0.010785,0.017828,0.003764,0.006398,0.003769,0.021347,0.005522,0.01432,0.024893,0.009032,0.003766,-0.001512,0.009042,0.017834,0.021372,-0.007669,0.007266,0.013438,0.009058,0.002015,0.016082,0.019619,-0.008582,0.007293,-0.003273,0.007281,0.016076,-0.001536,-0.00153,0.007277,-0.003279,0.005513,0.017825,0.005517,0.016096,0.009027,0.017845,0.020484,0.002001,0.007285,0.01607,0.017752,-0.008664,0.00541,0.013332,0.009818,0.001873,0.009814,0.007168,0.020381,0.009813,0.023885,0.003651,0.011573,0.023012,0.023882,0.00891,0.015975,0.021242,0.00275,0.016856,0.003653,0.020366,0.01071,0.007171,0.005418,0.02037,0.003642,0.007164,0.014233,-0.001618,0.033577,0.012465,0.002779,0.015961,0.01246,-0.002518,0.005398,0.007164,1,4,1
+T_zmag,0.39211,0.13513,0.04723,-0.17345,0.006449,-0.21635,-0.39754,-0.45393,-0.35084,-0.64262,-0.16831,0.1461,-0.67212,-0.5547,-0.59418,-0.64993,-0.87709,-0.71255,-0.58686,-0.60913,-0.39491,-0.54261,0.067551,0.37387,0.37666,-1.323,0.98504,1.0346,0.56992,0.27197,0.22973,0.21056,0.15761,0.36056,0.45005,0.21567,-0.017751,-0.082486,0.02835,0.093815,0.031356,-0.055494,-0.062826,-0.047365,-0.036943,-0.088704,-0.14018,-0.16127,-0.14617,-0.12973,-0.14058,-0.17478,-0.14706,-0.1251,-0.069755,0.004582,-0.054159,-0.11099,-0.06573,-0.012752,-0.016663,-0.013144,0.023837,-0.009225,0.016041,-0.000795,0.023679,0.007388,0.041848,0.011658,-0.021792,0.005067,-0.004071,0.03349,0.046949,0.014694,0.024478,0.03159,-0.004473,0.007986,-0.003874,-0.004873,0.018275,-0.006575,-0.006082,-0.017931,-0.000298,0.10388,0.029146,0.023507,0.02867,0.063026,0.10313,0.10027,0.073331,0.038485,0.018692,0.006337,-0.006694,-0.020073,-0.017859,-0.038086,-0.035029,-0.03127,-0.01731,0.005152,-0.054304,-0.049314,0.022171,0.034904,0.034,0.00437,-0.0068,0.024938,0.049484,0.017127,0.007896,0.010541,0.020188,0.066343,-0.011303,0.046953,-0.021976,-0.037287,0.013498,8,3,1
+RL_zmag,0.15432,0.1804,0.19911,0.22632,0.25956,0.26658,0.25555,0.25024,0.23626,0.20541,0.18692,0.16313,0.13553,0.10816,0.084072,0.061035,0.041324,0.017779,-0.004934,-0.00472,0.014563,0.048108,0.086554,0.12014,0.14035,0.14107,0.12824,0.11451,0.09001,0.052011,0.025704,0.001471,-0.007906,0.005688,0.026877,0.024716,0.006411,0.002226,-0.031559,-0.07119,-0.10146,-0.12892,-0.15337,-0.1791,-0.20091,-0.21598,-0.22701,-0.23681,-0.24663,-0.25162,-0.24801,-0.24004,-0.21322,-0.17622,-0.12639,-0.087445,-0.04003,-0.003726,0.011847,0.014963,-0.001314,-0.008493,0.003577,-0.003483,-0.018048,-0.028704,-0.02617,-0.031543,-0.041468,-0.050599,-0.059972,-0.067014,-0.067665,-0.066384,-0.06771,-0.067211,-0.064329,-0.058603,-0.057955,-0.058211,-0.055985,-0.054029,-0.061071,-0.067559,-0.070117,-0.066638,-0.053826,-0.024655,0.009013,0.011736,0.007855,0.009178,0.016711,0.030134,0.048623,0.066812,0.080052,0.07915,0.073858,0.073593,0.080235,0.080487,0.076461,0.07648,0.088959,0.10775,0.12163,0.13123,0.14316,0.15232,0.164,0.17384,0.18909,0.20694,0.22398,0.24075,0.26083,0.27677,0.29123,0.29981,0.29864,0.29309,0.28809,0.28678,0.28467,8,1,1
+RA_zmag,-0.29142,-0.22589,-0.098233,0.001665,-0.008271,-0.060571,-0.041036,0.008032,-0.011372,-0.14034,-0.24382,-0.34825,-0.1895,-0.17105,-0.18664,-0.20378,-0.30248,-0.36628,-0.39014,-0.29552,-0.23619,-0.20216,-0.20676,-0.277,-0.23939,-0.24569,-0.28919,-0.34612,-0.34168,-0.40346,-0.40112,-0.49268,-0.56365,-0.56004,-0.62527,-0.60725,-0.64142,-0.6018,-0.51895,-0.50083,-0.49802,-0.41662,-0.34279,-0.29344,-0.25372,-0.18596,-0.10532,-0.099506,-0.042933,-0.029523,-0.063349,0.013007,0.03973,0.036713,0.10827,0.10608,0.071849,0.003413,-0.006851,-0.068055,-0.050258,-0.047072,0.042929,0.036541,0.065023,0.10484,0.15004,0.18082,0.22072,0.23502,0.29329,0.37014,0.43155,0.52306,0.61369,0.65778,0.72594,0.78234,0.85714,0.86414,0.87889,0.93548,0.87067,0.89976,0.89453,0.8653,0.78081,0.68536,0.60578,0.49863,-0.15197,-0.58776,-0.2204,0.22027,0.23976,-0.16923,-0.29533,-0.012785,0.14581,0.050513,-0.015391,-0.000341,-0.007636,-0.022636,-0.29339,-0.26559,-0.19295,-0.073325,-0.066727,-0.12098,-0.29672,-0.16574,-0.14684,-0.12302,-0.081307,-0.052761,-0.082324,-0.10529,-0.050877,-0.15134,-0.51749,-0.67015,-0.86985,-0.78326,-0.82961,17,7,1
diff --git a/tests/core/test_dsa.py b/tests/core/test_dsa.py
new file mode 100755
index 000000000..c0c854bc7
--- /dev/null
+++ b/tests/core/test_dsa.py
@@ -0,0 +1,124 @@
+"""
+Unit tests for the DSADataset and ActivityClassification classes.
+
+Author:
+    Ran You
+"""
+
+import unittest
+from pathlib import Path
+
+from pyhealth.datasets import DSADataset
+from pyhealth.tasks import ActivityClassification
+
+class TestDSADataset(unittest.TestCase):
+    """Test cases for DSADataset."""
+
+    @classmethod
+    def setUpClass(cls):
+        """Set up test resources path."""
+        cls.test_resources = Path(__file__).parent.parent.parent / "test-resources" / "dsa"
+
+    def test_dataset_initialization(self):
+        """Test that the dataset initializes correctly."""
+        dataset = DSADataset(root=str(self.test_resources))
+        self.assertIsNotNone(dataset)
+        self.assertEqual(dataset.dataset_name, "dsa")
+
+    def test_stats(self):
+        """Test that stats() runs without error."""
+        dataset = DSADataset(root=str(self.test_resources))
+        dataset.stats()
+
+    def test_num_patients(self):
+        """Test the number of unique patient IDs."""
+        dataset = DSADataset(root=str(self.test_resources))
+        # Should have 8 unique patient IDs (1-8)
+        self.assertEqual(len(dataset.unique_patient_ids), 8)
+
+    def test_get_patient(self):
+        """Test retrieving a patient."""
+        dataset = DSADataset(root=str(self.test_resources))
+        patient = dataset.get_patient("5")
+        self.assertIsNotNone(patient)
+        self.assertEqual(patient.patient_id, "5")
+
+    def test_get_events(self):
+        """Test getting motion sensor data from a patient."""
+        dataset = DSADataset(root=str(self.test_resources))
+        patient = dataset.get_patient("4")
+        events = patient.get_events(event_type="activities")
+        # patient 5 has 2 samples in test data
+        self.assertEqual(len(events), 3)
+
+    def test_event_attributes(self):
+        """Test that event attributes are correctly loaded."""
+        dataset = DSADataset(root=str(self.test_resources))
+        patient = dataset.get_patient("5")
+        events = patient.get_events(event_type="activities")
+        event = events[0]
+
+        # Check that attributes exist
+        self.assertIn("activity", event)
+        self.assertIn("segment", event)
+        self.assertIn("sensor", event)
+
+    def test_default_task(self):
+        """Test that the default task is ActivityClassification."""
+        dataset = DSADataset(root=str(self.test_resources))
+        self.assertIsInstance(dataset.default_task, ActivityClassification)
+
+    def test_set_task(self):
+        """Test setting and running the activity classification task."""
+        dataset = DSADataset(root=str(self.test_resources))
+        task = ActivityClassification()
+        samples = dataset.set_task(task)
+
+        # Should have samples for activities.
+        self.assertGreater(len(samples), 0)
+
+    def test_task_output_format(self):
+        """Test that task output has the correct format."""
+        dataset = DSADataset(root=str(self.test_resources))
+        task = ActivityClassification()
+        samples = dataset.set_task(task)
+
+        if len(samples) > 0:
+            sample = samples[0]
+            self.assertIn("patient_id", sample)
+            self.assertIn("T_xacc", sample)
+            self.assertIn("LA_ygyro", sample)
+            self.assertIn("LL_zmag", sample)
+            self.assertIn("label", sample)
+
+
+class TestActivityClassification(unittest.TestCase):
+    """Test cases for ActivityClassification task."""
+
+    def test_task_attributes(self):
+        """Test task class attributes."""
+        task = ActivityClassification()
+        self.assertEqual(task.task_name, "ActivityClassification")
+        self.assertIn("T_xacc", task.input_schema)
+        self.assertIn("LA_ygyro", task.input_schema)
+        self.assertIn("LL_zmag", task.input_schema)
+        self.assertIn("label", task.output_schema)
+
+    def test_input_schema(self):
+        """Test input schema definition."""
+        task = ActivityClassification()
+        
+        for x in ["T", "LA", "RA", "LL", "RL"]: 
+            for y in ["x", "y", "z"]: 
+                for z in ["acc", "gyro", "mag"]: 
+                    feature = f"{x}_{y}{z}"
+                    self.assertEqual(task.input_schema[feature], "sequence")
+
+    def test_output_schema(self):
+        """Test output schema definition."""
+        task = ActivityClassification()
+        self.assertEqual(task.output_schema["label"], "multiclass")
+
+
+if __name__ == "__main__":
+    unittest.main()